Look ahead of links/alter links

ABSTRACT

A computationally-implemented method comprises obtaining at least a portion of data from a data source, determining a content of the data, determining an acceptability of an effect of content of the data at least in part via at least two virtual machine representations of at least a part of a real machine having at least one end-user specified preference, at least one of the at least two virtual machine representations operating at least in part on an individual core of a multi-core system, and displaying at least one data display option based on the determining an acceptability of a content of the data.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is related to and claims the benefit of theearliest available effective filing date(s) from the following listedapplication(s) (the “Related Applications”) (e.g., claims earliestavailable priority dates for other than provisional patent applicationsor claims benefits under 35 USC §119(e) for provisional patentapplications, for any and all parent, grandparent, great-grandparent,etc. applications of the Related Application(s)).

RELATED APPLICATIONS

1. For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of United States PatentApplication entitled LOOK AHEAD OF LINKS/ALTER LINKS, naming Gary W.Flake as the first named inventor, filed 21 Dec. 2007, application Ser.No. 12/005,064, which is currently co-pending, or is an application ofwhich a currently co-pending application is entitled to the benefit ofthe filing date.

2. For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of United States PatentApplication entitled LOOK AHEAD OF LINKS/ALTER LINKS, naming Gary W.Flake as the first named inventor, filed Dec. 27, 2007, application Ser.No. 12/005,637, which is currently co-pending, or is an application ofwhich a currently co-pending application is entitled to the benefit ofthe filing date.

The United States Patent Office (USPTO) has published a notice to theeffect that the USPTO's computer programs require that patent applicantsreference both a serial number and indicate whether an application is acontinuation or continuation-in-part. Stephen G. Kunin, Benefit ofPrior-Filed Application, USPTO Official Gazette Mar. 18, 2003, availableat http://www.uspto.gov/web/offices/com/sol/og/2003/week11/patbene.htm.The present Applicant has provided above a specific reference to theapplication(s) from which priority is being claimed as recited bystatute. Applicant understands that the statute is unambiguous in itsspecific reference language and does not require either a serial numberor any characterization, such as “continuation” or“continuation-in-part,” for claiming priority to U.S. patentapplications. Notwithstanding the foregoing, Applicant understands thatthe USPTO's computer programs have certain data entry requirements, andhence Applicant is designating the present application as acontinuation-in-part of its parent applications as set forth above, butexpressly points out that such designations are not to be construed inany way as any type of commentary and/or admission as to whether or notthe present application contains any new matter in addition to thematter of its parent application(s).

All subject matter of the Related Application and of any and all parent,grandparent, great-grandparent, etc. applications of the RelatedApplications is incorporated herein by reference to the extent suchsubject matter is not inconsistent herewith.

BACKGROUND

Web sites often contain links to other web sites enabling a user tonavigate from one web site to another. Certain links may contain datathat may compromise security and/or privacy. Certain links may containdata that a user may not desire to view.

SUMMARY

A computationally implemented method includes, but is not limited to:obtaining at least a portion of data from a data source; determining acontent of the data; determining an acceptability of an effect ofcontent of the data at least in part via at least two virtual machinerepresentations of at least a part of a real machine having at least oneend-user specified preference, at least one of the at least two virtualmachine representations operating at least in part on an individual coreof a multi-core system; and displaying at least one data display optionbased on the determining an acceptability of a content of the data. Inaddition to the foregoing, other computationally implemented methodaspects are described in the claims, drawings, and text forming a partof the present disclosure.

In one or more various aspects, related systems include but are notlimited to circuitry and/or programming for effecting the hereinreferenced method aspects; the circuitry and/or programming can bevirtually any combination of hardware, software, and/or firmwareconfigured to effect the herein referenced method aspects depending uponthe design choices of the system designer.

A computationally implemented system includes, but is not limited to:means for obtaining at least a portion of data from a data source; meansfor determining a content of the data; means for determining anacceptability of an effect of content of the data at least in part viaat least two virtual machine representations of at least a part of areal machine having at least one end-user specified preference, at leastone of the at least two virtual machine representations operating atleast in part on an individual core of a multi-core system; and meansfor displaying at least one data display option based on the determiningan acceptability of a content of the data. In addition to the foregoing,other system aspects are described in the claims, drawings, and textforming a part of the present disclosure.

A computationally implemented system includes, but is not limited to:circuitry for obtaining at least a portion of data from a data source;circuitry for determining a content of the data; circuitry fordetermining an acceptability of an effect of content of the data atleast in part via at least two virtual machine representations of atleast a part of a real machine having at least one end-user specifiedpreference, at least one of the at least two virtual machinerepresentations operating at least in part on an individual core of amulti-core system; and circuitry for displaying at least one datadisplay option based on the determining an acceptability of a content ofthe data. In addition to the foregoing, other system aspects aredescribed in the claims, drawings, and text forming a part of thepresent disclosure.

The foregoing summary is illustrative only and is not intended to be inany way limiting. In addition to the illustrative aspects, embodiments,and features described above, further aspects, embodiments, and featureswill become apparent by reference to the drawings and the followingdetailed description.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1A illustrates an exemplary environment in which one or moretechnologies may be implemented.

FIG. 1B illustrates an operational view of a real machine in which atleast a portion of the system illustrated in FIG. 1A has beenimplemented.

FIG. 1C illustrates an operational view of a real machine in which atleast a portion of the system illustrated in FIG. 1A has beenimplemented.

FIG. 1D illustrates an operational view of a real machine in which atleast a portion of the system illustrated in FIG. 1A has beenimplemented.

FIG. 2 illustrates an operational flow representing example operationsrelated to providing acceptable data content to a real machine.

FIG. 3 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 4 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 5 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 6 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 7 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 8 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 9 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 10 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 11 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 12 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 13 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 14 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 15 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 16 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 17 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 18 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 19 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 20 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 21 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 22 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 23 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 24 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 25 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 26 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 27 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 28 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 29 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 30 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 31 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 32 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 33 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 34 illustrates an alternative embodiment of the operational flow ofFIG. 2.

FIG. 35 illustrates an alternative embodiment of the operational flow ofFIG. 2.

DETAILED DESCRIPTION

In the following detailed description, reference is made to theaccompanying drawings, which form a part hereof. In the drawings,similar symbols typically identify similar components, unless contextdictates otherwise. The illustrative embodiments described in thedetailed description, drawings, and claims are not meant to be limiting.Other embodiments may be utilized, and other changes may be made,without departing from the spirit or scope of the subject matterpresented here.

Referring to FIG. 1A, a system 100 related to looking ahead for data isillustrated. The system 100 may include a data obtainer engine 102, adata content determination engine 104, an Effect of contentacceptability determination engine 106, and a data content providerengine 108. Data content determination engine 104 may include a databaseexamination engine 112, a data traverser engine 114, and a local dataexamination engine 116. Effect of content acceptability determinationengine 106 may include a virtual machine module 118 including one ormore virtual machines 11, 12, and 13 and a user preference database 120.Data content provider engine 108 may include a data modification engine122 that may further include a data obfuscation engine 124 and a dataanonymization engine 126. Data content provider engine 108 may alsoinclude a data redirection engine 128.

FIG. 1B shows an operational view of real machine 130 (e.g., a desktop,notebook, or other type computing system, including or excluding one ormore peripheral devices) in which at least a portion of system 100 (FIG.1A) has been implemented. System 100 may be at least partiallyimplemented in a multi-core processor at least partially resident withinreal machine 130 (e.g., one or more virtual machines of virtual machinemodule 118 at least partially respectively implemented on one or morecores of a multi-core processor of real machine 130). System 100 mayalso be at least partially implemented in a multi-core processor atleast partially non-resident within real machine 130 (e.g., one or morevirtual machines of virtual machine module 118 at least partiallyrespectively implemented on one or more cores of a multi-core processorof a hosting site/machine/system physically distal from real machine130).

FIG. 1B depicts real machine 130 containing data 110 (e.g., a Web page)containing Link 1, Link 2, and Link 3. FIG. 1B illustrates an example inwhich at least a part of system 100 traverses Link 1, Link 2, and Link 3of data 110 via virtual machines 11, 12, and 13, which may be virtualmachine representations of real machine 130. In some instances, suchvirtual machine traversals are utilized to prospectively determine whatmight happen should real machine 130 be used to traverse such links. Forexample, determining how such traversal(s) might compare to one or moreuser-associated preferences of real machine 130 (e.g., that user 10prefers to visit sites having content acceptable to a definedorganization, such as a government; that user 10 prefers not to visitsites having malware or spyware; that user 10 prefers not to visit sitesthat reset real machine hardware options (e.g., audio/visualperipherals); that user 10 prefers not to visit sites that reset realmachine software options (e.g., proxy servers); etc.). User-associatedpreferences of real machine 130 may be stored in user preferencedatabase 120 (FIG. 1A) of Effect of content acceptability determinationengine 106 (FIG. 1A). User preference database 120 may contain userpreferences with respect to content of the real machine 130, hardware ofthe real machine 130, software of the real machine 130, and an operatingsystem of the real machine 130. User preference database 120 may be incommunication with virtual machine module 118 (FIG. 1A). Specifically,virtual machine module 118 (FIG. 1A) may receive user preferencedatabase information from user preference database 120 (FIG. 1A) andspawn a copy of at least a portion of user preference database 120 (FIG.1A) on at least one of virtual machines 11, 12, and/or 13.

FIG. 1B illustrates virtual machine 11. Virtual machine 11 may beillustrated as included in virtual machine module 118 (FIG. 1A) ofEffect of content acceptability determination engine 106 (FIG. 1A). FIG.1B shows virtual machine 11 encompassing a virtual machinerepresentation of real machine 130, post (e.g., subsequent to)activation of Link 1 (e.g., as at least a part of real machine 130 wouldexist had link 1 actually been traversed on real machine 130). FIG. 1Bdepicts virtual machine 11 including a virtual machine representation ofthe content of the real machine 130 post activation of Link 1. Examplesof such content include a movie, music file, a script (e.g., Java scriptor Active X control), a markup language, an email, etc. downloaded ontoreal machine 130 from one or more sources associated withactivation/traversal of Link 1.

FIG. 1B also illustrates virtual machine 11 including a virtual machinerepresentation of software (e.g., a state of software) of the realmachine 130 post (e.g., subsequent to) activation of Link 1. Examples ofsuch software might include a commercial word processing program orsuite of programs (e.g., Microsoft® Office for Windows), an open sourceWeb browser (e.g., Mozilla's Firefox® Browser), an AJAX mash up (e.g.,an executing JavaScript™ and/or data obtained by same via an XML-likescheme), or a commercial database management system (e.g., one or moreof Oracle Corporation's various products), a commercialanti-malware/spyware program (e.g., such as those of SymantecCorporation or McAfee, Inc.), etc.

FIG. 1B also illustrates virtual machine 11 including a virtual machinerepresentation of hardware (e.g., a state of the hardware) of the realmachine 130 post activation of Link 1. Examples of such hardware mightinclude all or part of a chipset (e.g., data processor and/or graphicsprocessor chipsets such as those of Intel Corporation and/or NvidiaCorporation), a memory chip (e.g., flash memory and/or random accessmemories such as those of Sandisk Corporation and/or SamsungElectronics, Co., LTD), a data bus, a hard disk (e.g., such as those ofSeagate Technology, LLC), a network adapter (e.g., wireless and/or wiredLAN adapters such as those of Linksys and/or CiscoTechnology, Inc.),printer, a removable drive (e.g., flash drive), a cell phone, etc.

FIG. 1B also illustrates virtual machine 11 including a virtual machinerepresentation of an operating system (e.g., a state of an operatingsystem and/or network operating system) of the real machine 130 postactivation of Link 1. Examples of such an operating system might includea computer operating system (e.g., Microsoft® Windows 2000, Unix, Linux,etc) and/or a network operating system (e.g., the Internet OperatingSystem available from Cisco Technology, Inc. Netware® available fromNovell, Inc., and/or Solaris available from Sun Microsystems, Inc.).

FIG. 1B also illustrates that virtual machine 11 may run on core 11 of amulti-core processor. In addition to the herein, those skilled in theart will appreciate that the virtual machine representations discussedherein are not limited to specific examples described, but insteadinclude any components of real machine 130 as such might be understoodin the art Examples of the foregoing would include firmware, logicassociated with display units, logic associated with robotics,application specific integrated circuits, etc.

As noted, in some instances system 100 may traverse (e.g., view) linksof data 110 via one or more virtual machine representations of at leasta part of real machine 130. Accordingly, FIG. 1B shows virtual machine12 encompassing a virtual machine representation of real machine 130(e.g., one or more states of one or more components associated with realmachine 130), post activation of Link 2. FIG. 1B depicts virtual machine12 at least partly running on core 12 of a multi-core processor. Virtualmachine module 118 (FIG. 1A) of Effect of content acceptabilitydetermination engine 106 (FIG. 1A) may be illustrated to include virtualmachine 12. FIG. 1B also illustrates virtual machine 12 may include avirtual machine representation of content (e.g., a video) of realmachine 130 post activation of Link 2, a virtual machine representationof software (e.g., Microsoft Office for Windows) of real machine 130post activation of Link 2, a virtual machine representation of hardware(e.g., the circuitry or processor of the real machine) of real machine130 post activation of Link 2, and a virtual machine representation ofoperating system (e.g., Microsoft Windows 2000, XP, Vista) of realmachine 130 post activation of Link 2.

As noted, in some instances system 100 may traverse links of data 110via one or more virtual machine representations of at least a part ofreal machine 130. Accordingly, FIG. 1B shows virtual machine 13encompassing a virtual machine representation of real machine 130, postactivation of Link 3 (e.g., representative of one or more states of oneor more hardware/software/firmware components of/resident within realmachine 130). The foregoing constitutes one example of how system 100may use virtual machine 13 to traverse Link 3 (e.g., a link relating toa list of or links to information on architectural building styles).Virtual machine module 118 (FIG. 1A) of Effect of content acceptabilitydetermination engine 106 (FIG. 1A) may include virtual machine 13. FIG.1B further illustrates virtual machine 13 may include a virtual machinerepresentation of the content (e.g., a markup language) of the realmachine 130 post activation of Link 3, a virtual machine representationof the software (e.g., Unix) of the real machine 130 post activation ofLink 3, a virtual machine representation of the hardware (e.g., a harddisk) of the real machine 130 post activation of Link 3, and a virtualmachine representation of the operating system (e.g., Solaris OperatingSystem) of the real machine 130 post activation of Link 3. FIG. 1B showsthat virtual machine 13 may be run on core 13 of a multi-core processor.

Upon traversal of links 1, 2, and 3 by virtual machines 11, 12, and 13,respectively, each of virtual machines 11, 12, and 13 may determinewhether an effect of the data content is acceptable to a user based on ausers preferences. At least one of virtual machines 11, 12, and/or 13may compare the traversed data to one or more user preferences stored ina user preference database 120 (FIG. 1A). User preference databaseinformation may be communicated to virtual machine module 118 (FIG. 1A)and a copy of at least a portion of user preference database 120 may bespawned (e.g., generated) on at least one of virtual machines 11, 12,and/or 13. Virtual machines 11, 12, and 13 may communicate the resultsof a respective comparison of activation of a link (e.g., loading atleast a portion of a link's content onto a virtual machine 11, 12, 13)to a user preference (e.g., a preference not to load malware onto auser's real machine) to virtual machine module 118 (FIG. 1A). Virtualmachine module 118 (FIG. 1A) may communicate the results of a comparisonof activation of a link to a user preference to Effect of contentacceptability determination engine 106 (FIG. 1A). Effect of contentacceptability determination engine 106 may communicate the comparison tothe data content provider engine 108 (FIG. 1A). The data contentprovider engine 108 may then provide the results (e.g., one or moreweblinks approved for viewing) to a real machine 130 (e.g., a computingdevice with or without associated peripherals) that may be viewable by auser 10 on a display.

FIG. 1C shows a partial follow-on operational view of real machine 130(e.g., a desktop, notebook, or other type computing system) in which atleast a portion of system 100 (FIG. 1A) has been implemented (e.g., afollow-on operational view of the systems/methods illustrated as in FIG.1B). Specifically, FIG. 1C shows a drill-down view of an example of thevirtual machine 11 including a virtual machine representation of thecontent of the real machine 130 post activation of Link 1 (e.g., adrill-down on the systems/methods shown/described in relation to FIG.1B). In this drill down example, depicted is the virtual machinerepresentation of the content of the real machine 130 post activation ofLink 1. In the example shown, the content is depicted as data 110 havingLink 4, Link 5, and Link 6. As a specific example, data 110 could be aWeb page containing embedded Link 4 to an advertisement, Link 5 to avideo file, and Link 6 to a still image file.

In some instances, system 100 may use additional virtual machinerepresentations of at least a part of real machine 130 to prospectivelytraverse Link 4, Link 5, and Link 6. Accordingly, FIG. 1C illustratessystem 100 generating virtual machine representations of real machine130, used to traverse Links 4, 5, and 6, in the context of virtualmachines 21, 22, and 23, respectively. Those skilled in the art willthus appreciate that, in the example shown in FIG. 1C, system 100 iscreating second-order virtual machine representations to prospectivelyinvestigate the effects on the states of various components of realmachine 130 via sequential traversals of links. That is, the virtualmachine representations of real machine 130 encompassed in virtualmachine 21, virtual machine 22, and virtual machine 23 of FIG. 1C aregenerated by system 100 based on the first-order virtual machinerepresentation of virtual machine 11 as shown/described in relation toFIG. 1B.

Upon traversal of links 4, 5, and 6 by virtual machines 21, 22, and 23,respectively, each of virtual machines 21, 22, and 23 may determinewhether an effect of the data content is acceptable to a user based on auser's preferences. Virtual machines 21, 22, and 23 may compare thetraversed data to one or more user preferences stored in a userpreference database 120 (FIG. 1A). As previously described, userpreference database information may be communicated to virtual machinemodule 118 (FIG. 1A) and a copy of at least a portion of user preferencedatabase 120 may be spawned (e.g., generated) on at least one of virtualmachines 11, 12, and/or 13. Virtual machine 11 may then communicate userpreference database information to each of virtual machines 21, 22, and23, and a copy of a user preference database 120 (FIG. 1A) may bespawned on each of virtual machines 21, 22, and 23. Virtual machines 21,22, and 23 may communicate the results of a respective comparison ofactivation of a link (e.g., loading at least a portion of a link'scontent onto a virtual machine 21, 22, and/or 23) to a user preference(e.g., a preference to prevent installation of a rootkit onto a user'sreal machine) to virtual machine 11. Virtual machine 11 may communicatethe results of a comparison to virtual machine module 118 (FIG. 1A).Virtual machine module 118 (FIG. 1A) may communicate the results of acomparison of activation of a link to a user preference to effect ofcontent acceptability determination engine 106 (FIG. 1A). Effect ofcontent acceptability determination engine 106 may communicate thecomparison to the data content provider engine 108 (FIG. 1A). The datacontent provider engine 108 may then provide the results (e.g., one ormore weblinks approved for viewing) to a real machine 130 (e.g., acomputing device with or without associated peripherals) that may beviewable by a user 10 on a display.

FIG. 1C shows virtual machine 21 encompassing a virtual machinerepresentation of real machine 130 post (e.g., subsequent to) asequential activation of Link 1 (e.g., as shown on FIG. 1B) then Link 4(e.g., as shown on FIG. 1C). FIG. 1C depicts that in one instancevirtual machine 21 may be run on core 31 of a multi-core processor. FIG.1C depicts system 100 traversing Link 4 via a virtual machinerepresentation of real machine 130 encompassed within virtual machine21. Accordingly, FIG. 1C illustrates virtual machine 21 including avirtual machine representation of content (e.g., a movie, web page,music file, etc.) of the real machine 130 post sequential activation ofLink 1 then Link 4, a virtual machine representation of the software(e.g., Windows Media Player, Apple's Quicktime Player, etc.) of the realmachine 130 post sequential activation of Link 1 then Link 4, a virtualmachine representation of the hardware (e.g., the circuitry or processorof the real machine) of the real machine 130 post sequential activationof Link 1 then Link 4, and a virtual machine representation of theoperating system (e.g., Linux) of the real machine 130 post sequentialactivation of Link 1 then Link 4.

FIG. 1C shows virtual machine 22 encompassing a virtual machinerepresentation of real machine 130 post (e.g., subsequent to) asequential activation of Link 1 (e.g., as shown on FIG. 1B) then Link 5(e.g., as shown on FIG. 1C). FIG. 1C depicts that in one instancevirtual machine 22 may be run on core 32 of a multi-core processor. FIG.1C depicts system 100 traversing Link 5 via a virtual machinerepresentation of real machine 130 encompassed within virtual machine22. Accordingly, FIG. 1C illustrates virtual machine 22 including avirtual machine representation of content (e.g., a graphical image, atext file, an email, etc) of the real machine 130 post (e.g., subsequentto) sequential activation of Link 1 then Link 5, a virtual machinerepresentation of software (e.g., an AJAX mashup) of the real machine130 post sequential activation of Link 1 then Link 5, a virtual machinerepresentation of hardware (e.g., a network adapter) of the real machine130 post sequential activation of Link 1 then Link 5, and a virtualmachine representation of an operating system (e.g., Mac OS/X) of thereal machine 130 post sequential activation of Link 1 then Link 5.

FIG. 1C shows virtual machine 23 may be a virtual machine representationof real machine 130 post (e.g., subsequent to) sequential activation ofLink 1 (e.g., FIG. 1B) then Link 6 (e.g., FIG. 1C). FIG. 1C depicts thatin one instance virtual machine 23 may be run on core 33 of a multi-coreprocessor. System 100 is shown using virtual machine 23 to traverse Link6. FIG. 1C further illustrates virtual machine 23 encompassing a virtualmachine representation of the content (e.g., a music file) of the realmachine 130 post sequential activation of Link 1 then Link 6, a virtualmachine representation of the software (e.g., a commercial databasemanagement system) of the real machine 130 post sequential activation ofLink 1 then Link 6, a virtual machine representation of the hardware(e.g., a removable drive) of the real machine 130 post sequentialactivation of Link 1 then Link 6, and a virtual machine representationof the operating system (e.g., GNU, Berkeley Software Distribution) ofthe real machine 130 post sequential activation of Link 1 then Link 6(e.g., as such might appear after activation of a link installed by arootkit via malware/spyware).

Those skilled in the art will appreciate that system 100 may generate asmany virtual machines as necessary to traverse individual links ofinterest, that individual virtual machines may run on a core of amulti-core processor comprising any number of individual cores, and thatthe examples herein are used for sake of clarity. Those skilled in theart will appreciate that examples used herein are meant to be indicativeof the fact that system 100 can run in whole or in part on proximatemulti-core machines and/or distal or multi-core machines, on distributedcomputing systems (e.g., GRID or clustered), on local computing systems,or hosted computing systems, etc.

FIG. 1D shows a representative view of an implementation of real machine130 (e.g., a desktop, notebook, or other type computing system, and/orone or more peripheral devices). FIG. 1D illustrates thatimplementations of real machine 130 may include all/part of computingdevice 132 and/or all/part of one or one or more peripherals associatedcomputing device 132. The computing device 132 may be any device capableof processing one or more programming instructions. For example, thecomputing device 132 may be a desktop computer, a laptop computer, anotebook computer, a mobile phone, a personal digital assistant (PDA),combinations thereof, and/or other suitable computing devices.

As noted, in some instances, real machine 130 may also include at leasta portion of one or more peripheral devices connected/connectable (e.g.,via wired, waveguide, or wireless connections) to real machine 130.Peripheral devices may include one or more printers 134, one or more faxmachines 136, one or more peripheral memory devices 138 (e.g., flashdrive, memory stick), one or more network adapters 139 (e.g., wired orwireless network adapters), one or more music players 140, one or morecellular telephones 142, one or more data acquisition devices 144 (e.g.,robots) and/or one or more device actuators 146 (e.g., an hydraulic arm,a radiation emitter, or any other component(s) of industrial/medicalsystems).

FIG. 2 illustrates an operational flow 200 representing exampleoperations related to FIGS. 1A, 1B, 1C and 1D. In FIG. 2 and infollowing figures that include various examples of operational flows,discussion and explanation may be provided with respect to theabove-described examples of FIGS. 1A, 1B, 1C, and 1D and/or with respectto other examples and contexts. However, it should be understood thatthe operational flows may be executed in a number of other environmentsand contexts, and/or in modified versions of FIGS. 1A, 1B, 1C, and 1D.Also, although the various operational flows are presented in thesequence(s) illustrated, it should be understood that the variousoperations may be performed in other orders than those which areillustrated, or may be performed concurrently.

After a start operation, the operational flow 200 moves to an operation210. Operation 210 depicts obtaining at least a portion of data from adata source (e.g., a server accessible from the internet). For example,FIG. 1A shows a data obtainer engine 102. Data obtainer engine mayobtain (e.g., download) data 110 (e.g., a web page) from a data sourcesuch as a computer accessible from the internet. Specifically, data 110may be web content obtained from the World Wide Web via a computingdevice accessible from the internet. For example, data obtainer engine102 may set a URL and add a querystring value to the URL. Data obtainerengine 102 may then make a request to the URL and scan the responsereceived from the URL. Data 110 may be a web site or web page containingone or more links to additional web sites, such as shown, for example,in FIG. 1B and/or FIG. 1C. Data 110 may in some instances be textual, atwo-dimensional or three-dimensional image, audible, or videorepresentations, which in some instances may entail programming codesuch as html, javascript, C, C++, or any other programming code capableof producing text, visual images, audio content, video content or anycombination of text, visual images, audible content and video content.Data obtainer engine 102 may transmit at least a portion of obtaineddata to data content determination engine 104.

Then, operation 220 depicts determining a content of the data (e.g.,detecting if an image in a link is in jpeg format). Continuing theexample above, FIG. 1A shows a data content determination engine 104.Data content determination engine 104 may determine the content (e.g., aformat/protocol) of at least a portion of the data 110 obtained from thedata source by the data obtainer engine 102. Data content determinationengine 104 may isolate (e.g., quarantine) at least a portion of the data110 prior to determining data content (e.g., video is a Real Networksvideo). Data content determination engine 104 may utilize, for example,pointers or other file format identifiers to determine data content. Forexample, data content determination engine 104 may locate a formatspecification document within the data content to determine how data 110is encoded or determine a format of a data content by determining afilename extension (e.g., .htm, .gif, .wav) for the data content or afile format identifier (e.g., identifying a file format according toorigin and file category) for the data content. Data contentdetermination engine 104 may provide Effect of content acceptabilitydetermination engine 106 with determined data content information toassist Effect of content acceptability determination engine 106 todetermine an effect of the content. Data content formats may be anytext, image, audio, and/or video file formats including, but not limitedto JPEG format, or other format designed to store static photographicimages, GIF format, or other format supporting storage of both stillimages and/or animations, QuickTime format, Windows Media Player format,or other format configured to store multiple multimedia formats.

Then, operation 230 depicts determining an acceptability of an effect ofcontent of the data at least in part via at least two virtual machinerepresentations of at least a part of a real machine having at least oneend-user specified preference, at least one of the at least two virtualmachine representations operating at least in part on an individual coreof a multi-core system (e.g., a multi-core processor, a multi-coresystem on a chip, etc.). Continuing the example above, Effect of contentacceptability determination engine 106 (FIG. 1A) may receive data and anassociated data content determination (e.g., data content is a WindowsMedia Audio format audio file) from data content determination engine104 post obtaining of data by data obtainer engine 102 and transfer ofobtained data to data content determination engine 106. Data contentdetermination engine may provide Effect of content acceptabilitydetermination engine 106 with information regarding a format/protocol ofcontent at least a portion of the data. Effect of content acceptabilitydetermination engine 106 may utilize format/protocol information todetermine whether Effect of content acceptability determination engineshould call a specific database or library (e.g., a Windows Media Playerlibrary) to obtain file format information. File format information maybe utilized to compare received data content to data stored in alibrary. Effect of content acceptability determination engine 106 (FIG.1A) may utilize, for example, virtual machine 12 (FIG. 1A) spawned byvirtual machine module 118 on an individual core of a multi-core systemto determine whether data associated with Link 2 would result in achange in the operating system of real machine 130 contra to userpreferences regarding the operating system as reflected by userpreference database 120.

Then, operation 240 depicts displaying at least one data display optionbased on the determining an acceptability of a content of the data.Continuing the example above, Effect of content acceptabilitydetermination engine 106 (FIG. 1A) may transfer at least one effect ofcontent acceptability determination (e.g., content contains malware) tothe data content provider engine 108. Data content provider engine 108may provide at least one data display option (e.g., displaying a messagethat content will not display) to a user based on the received effect ofcontent acceptability determination. In one example, data providerengine 108 provides data via placing the data on a visual display, wherethe content is such that it meets one or more thresholds associated withthe effect of content acceptability determination 106. Provided data maybe a list of web links, a web page, or other data that either have beendeemed acceptable by effect of content acceptability determinationengine 106 or that have been modified (e.g., obfuscated), such as bydata modification engine 122, such that the to-be-displayed content isjudged acceptable under user preferences. Provided data may be modifiedvia the data modification engine 122. For instance, provided data may beobfuscated via the data obfuscation engine 124 (e.g., at least a portionof the displayed data may be blurred out or disabled), or provided datamay be anonymized via the data anonymization engine 126 (e.g., at leasta portion of the data may be deleted entirely). Data content providerengine 108 (FIG. 1A) may receive at least one display instruction (e.g.,OK to display links 1 and 2) from at least one component of Effect ofcontent acceptability determination engine 106 (FIG. 1A) for at least aportion of data to be displayed. For instance, at least one of virtualmachines 11, 12, and/or 13 may include one or more instructiongenerating modules configured to provide an instruction to the Effect ofcontent acceptability determination engine 106 after a comparison of anactivation of a link to a user preference stored in a copy of the userpreference database 120 (FIG. 1A) spawned on the virtual machine 11, 12,and/or 13. Such instruction may include an instruction to the datacontent provider engine 108 to prevent the data content provider engine108 from displaying data that may configure a hardware profile of realmachine 130 counter to anti-viral settings stored in the user preferencedatabase 120 (FIG. 1A), or an instruction to the data content providerengine 108 to prevent the data content provider engine 108 fromdisplaying data that may configure an operating system of real machine130 counter to a previous operating system of the real machine 130(e.g., determine if a rootkit has been installed).

FIG. 3 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 3 illustrates example embodiments where theoperation 220 may include at least one additional operation. Additionaloperations may include an operation 302, an operation 304, and/or anoperation 306.

The operation 302 illustrates examining a database of known content fordata content information. Continuing the example above, data contentdetermination engine 104 (FIG. 1A) may transmit at least a portion ofdata content to a database examination engine 112 to extract datacontent information from at least a portion of the data. A databaseexamination engine 112 may examine (e.g., scan) a database (e.g.,information obtained from a storage server) of known data (e.g., adatabase of known audio formats) and compare the known data to the data110 to determine data content (e.g., audio content is in mp3 format).Data content determination may be transmitted from the databaseexamination engine 112 to the data content determination engine 104, andthe data content determination engine 104 may subsequently transmit thedata content determination to the Effect of content acceptabilitydetermination engine 106. Database examination engine 112 may beconfigured to examine a database of data provided, for example, by adata provider service or a database of data stored on a real machine130. For instance, a database may include a list of links viewed by auser or pre-approved by a user based on one or more user-specifiedpreferences, such as links from a specific source of information (e.g.,the Roman Catholic Church).

The operation 304 illustrates traversing at least a portion of the datain real time. Continuing the example above, data content determinationengine 104 (FIG. 1A) may transmit at least a portion of data content toa data traverser engine 114 to extract data content information from atleast a portion of the data. A data traverser engine 114 may traverse(e.g., parse) at least a portion of the data (e.g., a portion of a webpage) to determine a format for at least a portion of data content(e.g., an image format or video format) within the portion of the data.Data traversal may occur in real time (e.g., simultaneously as data isloading). Data content determination may be transmitted from the datatraverser engine 114 to the data content determination engine 104, andthe data content determination engine 104 may subsequently transmit thedata content determination to the Effect of content acceptabilitydetermination engine 106.

The operation 306 illustrates locally examining at least a portion ofthe data. Continuing the example above, data content determinationengine 104 may transmit at least a portion of data content to a localdata examination engine 116 (FIG. 1A) to extract data contentinformation from at least a portion of the data. A local dataexamination engine 116 may locally (e.g., on the real machine 130)examine (e.g., analyze) at least a portion of the data (e.g., one ormore pointers in the data) to determine data content (e.g., an audiofile is a .wav file). For instance, local data examination engine 116may view an amount of html source code to locate markers signifying theformat of at least a portion of data content. Data content determinationmay be transmitted from the local data examination engine 116 to thedata content determination engine 104. Data content determination engine104 may transmit a data content determination to the Effect of contentacceptability determination engine 106.

FIG. 4 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 4 illustrates example embodiments where theoperation 230 may include at least one additional operation. Additionaloperations may include an operation 402, an operation 404, and/or anoperation 406.

The operation 402 illustrates examining at least a portion of the datato locate references to additional content. Continuing the exampleabove, Effect of content acceptability determination engine 106 (FIG.1A) may receive a data content determination from data contentdetermination engine 104 post obtaining of data by data obtainer engine102 and communication of obtained data to data content determinationengine 104. Effect of content acceptability determination engine 106 maytransfer data received from data content determination engine 104following a determination of data content. Effect of contentacceptability determination engine 106 may transfer the data andassociated data content determination to the virtual machine module 118.Virtual machine module 118 (FIG. 1A) may spawn at least one virtualmachine 11, 12, and/or 13 and transfer the data and associated datacontent determination to at least one of virtual machines 11, 12, and/or13 (FIG. 1B). Each of virtual machines 11, 12, and/or 13 may examine(e.g., scan) at least a portion of data (e.g., an imbedded link on awebpage) to determine if the data references additional content (e.g.,one or more additional images). Additional content may be a web pagecomprising text and/or an image, a link to a web page, a video or anycombination of text, images, links to web pages, or videos. Virtualmachines 11, 12, and/or 13 may traverse additional data to determine anacceptability of an effect of the data content. Effect of contentacceptability determination may be communicated to Effect of contentacceptability determination engine 106 (FIG. 1A) that may communicate aneffect of content acceptability determination to a data provider engine108 (FIG. 1A).

The operation 404 illustrates determining whether the data referencesadditional data content information when loading. Continuing the exampleabove, Effect of content acceptability determination engine 106 mayreceive a data content determination from data content determinationengine 104 post obtaining of data by data obtainer engine 102 andcommunication of obtained data to data content determination engine 104.Effect of content acceptability determination engine 106 may transferdata received from data content determination engine 104 following adetermination of data content. Effect of content acceptabilitydetermination engine 106 may transfer the data and associated datacontent determination to the virtual machine module 118. Virtual machinemodule 118 (FIG. 1A) may spawn at least one virtual machine 11, 12,and/or 13 and transfer the data and associated data contentdetermination to at least one of virtual machines 11, 12, and/or 13. Anyof virtual machines 11, 12, and/or 13 may examine the data in real timeas it loads onto the virtual machine 11, 12, and/or 13 to determine ifthe data (e.g., Link 1) references additional data (e.g., Link 4) whenloading. For instance, if a link to a webpage immediately (e.g., as soonas the link is activated) references an additional link (e.g., toredirect a user), a virtual machine 11, 12, and/or 13 may determine thatsuch a reference to an additional link has been made. Virtual machines11, 12, and/or 13 may determine whether data references additional dataat any time when the data is loading. Effect of content acceptabilitydetermination engine 106 (FIG. 1A) may communicate an effect of contentacceptability determination to a data provider engine 108 (FIG. 1A).

The operation 406 illustrates issuing a request to a remote computer foradditional data content information. Continuing the example above,Effect of content acceptability determination engine 106 (FIG. 1A) mayreceive a data content determination from data content determinationengine 104 post obtaining of data by data obtainer engine 102 andcommunication of obtained data to data content determination engine 104.Effect of content acceptability determination engine 106 may transferdata received from data content determination engine 104 following adetermination of data content. Effect of content acceptabilitydetermination engine 106 may transfer the data and associated datacontent determination to the virtual machine module 118. Virtual machinemodule 118 (FIG. 1A) may spawn at least one virtual machine 11, 12,and/or 13 on at least one core of a multi-core system including, forexample, cores 11, 12, and/or 13 and transfer the data and associateddata content determination to at least one of virtual machines 11, 12,and/or 13 (FIG. 1B). Each of virtual machines 11, 12, and/or 13 mayexamine (e.g., scan) at least a portion of data (e.g., an imbedded linkon a webpage) to determine if the data references additional data (e.g.,one or more additional links). Additional data may be a web pagecomprising text and/or an image, a link to a web page, a video or anycombination of text, images, links to web pages, or videos. Virtualmachines 11, 12, and/or 13 may traverse additional data to determine anacceptability of an effect of the data content. Effect of contentacceptability determination may be communicated to Effect of contentacceptability determination engine 106 (FIG. 1A) that may communicate aneffect of content acceptability determination to a data provider engine108 (FIG. 1A).

FIG. 5 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 5 illustrates example embodiments where theoperation 230 may include at least one additional operation. Additionaloperations may include an operation 502, and/or an operation 504.

The operation 502 illustrates determining an acceptability of an effectof content of the data at least in part via at least two virtual machinerepresentations of at least a part of a real machine having at least oneend-user specified preference, at least one of the at least two virtualmachine representations operating at least in part on an individual coreof a multi-core system at least partially resident within a realmachine. Continuing the example above, Effect of content acceptabilitydetermination engine 106 (FIG. 1A) may receive a data contentdetermination from data content determination engine 104 post obtainingof data by data obtainer engine 102 and communication of obtained datato data content determination engine 104. Effect of contentacceptability determination engine 106 may transfer data received fromdata content determination engine 104 following a determination of datacontent. Effect of content acceptability determination engine 106 maytransfer the data and associated data content determination to thevirtual machine module 118. Virtual machine module 118 (FIG. 1A) mayspawn at least one virtual machine 11, 12, and/or 13 and transfer thedata and associated data content determination to at least one ofvirtual machines 11, 12, and/or 13. In one implementation, at least oneof virtual machines 11, 12, and/or 13 (FIG. 1B) may be generated on thereal machine 130 (e.g., as a subsystem of real machine 130).

The operation 504 illustrates determining an acceptability of an effectof content of the data at least in part via at least two virtual machinerepresentations of at least a part of a real machine having at least oneend-user specified preference, at least one of the at least two virtualmachine representations operating at least in part on an individual coreof a multi-core system at least partially non-resident within a realmachine. Continuing the example above, Effect of content acceptabilitydetermination engine 106 (FIG. 1A) may receive a data contentdetermination from data content determination engine 104 post obtainingof data by data obtainer engine 102 and communication of obtained datato data content determination engine 104. Effect of contentacceptability determination engine 106 may transfer data received fromdata content determination engine 104 following a determination of datacontent. Effect of content acceptability determination engine 106 maytransfer the data and associated data content determination to thevirtual machine module 118 including virtual machines 11, 12, and 13. Inone implementation, at least one of virtual machines 11, 12, and/or 13may be generated on a remote server, remote operating system orotherwise geographically distinct location with respect to the realmachine 130.

FIG. 6 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 6 illustrates example embodiments where theoperation 230 may include at least one additional operation. Additionaloperations may include an operation 602, and/or an operation 604.

The operation 602 illustrates determining an acceptability of an effectof content of the data at least in part via at least two virtual machinerepresentations of at least a portion of content of a real machine.Continuing the example above, Effect of content acceptabilitydetermination engine 106 (FIG. 1A) may receive a data contentdetermination from data content determination engine 104 post obtainingof data by data obtainer engine 102. Effect of content acceptabilitydetermination engine 106 may transfer data received from data contentdetermination engine 104 following a determination of data content.Effect of content acceptability determination engine 106 may transferthe data and associated data content determination to the virtualmachine module 118. Virtual machine module 118 (FIG. 1A) may spawn atleast one virtual machine 11, 12, and/or 13 and transfer the data andassociated data content determination to at least one of virtualmachines 11, 12, and/or 13. FIG. 1B shows virtual machines 11, 12, and13 encompassing a virtual machine representation of real machine 130,post (e.g., subsequent to) activation of Link 1, Link 2, and Link 3,respectively (e.g., as at least a part of real machine 130 would existhad Link 1, Link 2, and/or Link 3 actually been traversed on realmachine 130). FIG. 1B further depicts virtual machines 11, 12, and 13including a virtual machine representation of content of the realmachine 130 post activation of Link 1, Link 2, and/or Link 3,respectively. Examples of such content include a movie, music file, ascript (e.g., Java script or Active X control), a markup language, anemail, etc. downloaded onto real machine 130 from one or more sourcesassociated with activation/traversal of Link 1, Link 2, and/or Link 3.An example of determining an acceptability of an effect of the contentof the data at least in part via a virtual machine representation mayinclude determining an acceptability of an effect of the content of thedata at least in part via a virtual machine representation of at least aportion of the content of the real machine include determining whetheror not a video or image has been loaded onto, for example, the virtualmachine 11 after loading at least a portion of the data contained inLink 1.

The operation 604 illustrates determining an acceptability of an effectof content of the data at least in part via at least two virtual machinerepresentations of at least a portion of software of a real machine.Continuing the example above, Effect of content acceptabilitydetermination engine 106 (FIG. 1A) may receive a data contentdetermination from data content determination engine 104 post obtainingof data by data obtainer engine 102. Effect of content acceptabilitydetermination engine 106 may transfer data received from data contentdetermination engine 104 following a determination of data content.Effect of content acceptability determination engine 106 may transferthe data and associated data content determination to the virtualmachine module 118. Virtual machine module 118 (FIG. 1A) may spawn atleast one virtual machine 11, 12, and/or 13 and transfer the data andassociated data content determination to at least one of virtualmachines 11, 12, and/or 13. FIG. 1B shows virtual machines 11, 12, and13 encompassing a virtual machine representation of real machine 130,post (e.g., subsequent to) activation of Link 1, Link 2, and Link 3,respectively (e.g., as at least a part of real machine 130 would existhad Link 1, Link 2, and/or Link 3 actually been traversed on realmachine 130). FIG. 1B illustrates virtual machine 11 including a virtualmachine representation of software (e.g., a state of software, such as astate of Windows Media Player) of the real machine 130 post (e.g.,subsequent to) activation of Link 1. Examples of such software mightinclude a commercial word processing program or suite of programs (e.g.,Microsoft® Office for Windows), an open source Web browser (e.g.,Mozilla's Firefox® Browser), an AJAX mash up (e.g., an executingJavaScript™ and/or data obtained by same via an XML-like scheme), acommercial database management system (e.g., one or more of OracleCorporation's various products), a commercial anti-malware/spywareprogram (e.g., such as those of Symantec Corporation or McAfee, Inc.), amulti-media program (e.g., QuickTime) etc. An example of determining anacceptability of an effect of the content of the data at least in partvia a virtual machine representation may include determining anacceptability of an effect of the content of the data at least in partvia a virtual machine representation of at least a portion of thesoftware of the real machine include determining whether or not anunauthorized program or suite of programs (e.g., music downloadingsoftware) has been loaded, for example, onto virtual machine 12 afterloading at least a portion of the data contained in Link 2.

FIG. 7 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 7 illustrates example embodiments where theoperation 230 may include at least one additional operation. Additionaloperations may include an operation 702, and/or an operation 704.

The operation 702 illustrates determining an acceptability of an effectof content of the data at least in part via at least two virtual machinerepresentations of at least a portion of hardware of a real machine.Continuing the example above, Effect of content acceptabilitydetermination engine 106 (FIG. 1A) may receive a data contentdetermination from data content determination engine 104 post obtainingof data by data obtainer engine 102 and may transfer data received fromdata content determination engine 104 following a determination of datacontent. Effect of content acceptability determination engine 106 maytransfer the data and associated data content determination to thevirtual machine module 118. FIG. 1B illustrates virtual machine 11including a virtual machine representation of hardware (e.g., a state ofthe hardware) of the real machine 130 post activation of Link 1.Examples of such hardware might include all or part of a chipset (e.g.,data processor and/or graphics processor chipsets such as those of IntelCorporation and/or NvidiaCorporation), a memory chip (e.g., flash memoryand/or random access memories such as those of Sandisk Corporationand/or Samsung Electronics, Co., LTD), a data bus, a hard disk (e.g.,such as those of Seagate Technology, LLC), a network adapter (e.g.,wireless and/or wired LAN adapters such as those of Linksys and/orCiscoTechnology, Inc.), printer, a removable drive (e.g., flash drive),a cell phone, etc. An example of determining an acceptability of aneffect of the content of the data at least in part via a virtual machinerepresentation includes determining an acceptability of an effect of thecontent of the data at least in part via a virtual machinerepresentation of at least a portion of the hardware of the real machineincludes determining whether a network adapter on, for example, virtualmachine 12 has been disabled after loading at least a portion of thedata contained in Link 2.

The operation 704 illustrates determining an acceptability of an effectof content of the data at least in part via at least two virtual machinerepresentations of at least a portion of an operating system of a realmachine. Continuing the example above, Effect of content acceptabilitydetermination engine 106 (FIG. 1A) may receive a data contentdetermination from data content determination engine 104 post obtainingof data by data obtainer engine 102. Effect of content acceptabilitydetermination engine 106 may transfer data received from data contentdetermination engine 104 following a determination of data content.Effect of content acceptability determination engine 106 may transferthe data and associated data content determination to the virtualmachine module 118. Virtual machine module 118 (FIG. 1A) may spawn atleast one virtual machine 11, 12, and/or 13 and transfer the data andassociated data content determination to at least one of virtualmachines 11, 12, and/or 13. FIG. 1B shows virtual machines 11, 12, and13 encompassing a virtual machine representation of real machine 130,post (e.g., subsequent to) activation of Link 1, Link 2, and Link 3,respectively (e.g., as at least a part of real machine 130 would existhad link 1, link 2, and/or link 3 actually been traversed on realmachine 130). FIG. 1B illustrates virtual machine 11 including a virtualmachine representation of an operating system (e.g., a state of anoperating system and/or network operating system) of the real machine130 post activation of Link 1. Examples of such an operating systemmight include a computer operating system (e.g., Microsoft® Windows2000, Unix, Linux, etc) and/or a network operating system (e.g., theInternet Operating System available from Cisco Technology, Inc. Netware®available from Novell, Inc., and/or Solaris available from SunMicrosystems, Inc.). An example of determining an acceptability of aneffect of the content of the data at least in part via a virtual machinerepresentation includes determining an acceptability of an effect of thecontent of the data at least in part via a virtual machinerepresentation of at least a portion of an operating system of the realmachine include determining whether a portion of the operating system(e.g., Microsoft Vista) on for example, virtual machine 12 has beendisabled after loading at least a portion of the data contained in Link2.

FIG. 8 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 8 illustrates example embodiments where theoperation 230 may include at least one additional operation. Additionaloperations may include an operation 802, an operation 804, and/or anoperation 806.

The operation 802 illustrates determining an acceptability of an effectof content of the data at least in part via at least two virtual machinerepresentations of a real machine including at least a portion of acomputing device. FIG. 1D illustrates real machine 130 including atleast a part of a computing device 132. The computing device 132 may beany device capable of processing one or more programming instructions.For example, the computing device 132 may be a desktop computer, alaptop computer, a notebook computer, a mobile phone, a personal digitalassistant (PDA), combinations thereof, and/or other suitable computingdevices.

The operation 804 illustrates determining an acceptability of an effectof content of the data at least in part via at least two virtual machinerepresentations of a real machine including at least one peripheraldevice. Continuing the example above, Effect of content acceptabilitydetermination engine 106 (FIG. 1A) may receive a data contentdetermination from data content determination engine 104 post obtainingof data by data obtainer engine 102 and communication of obtained datato data content determination engine 104. FIG. 1A further illustratesthe Effect of content acceptability determination engine 106 furtherincluding a virtual machine module 118 and a user preference database120. Virtual machine module 118 may spawn at least one of virtualmachines 11, 12, and/or 13 that may be a virtual machine representationof at least a part of real machine 130. Real machine 130 (FIG. 1B) mayinclude at least one peripheral device. For instance, FIG. 1Dillustrates real machine 130 including at least one peripheral device134-146. FIG. 1D shows a representative view of an implementation ofreal machine 130 (e.g., a desktop, notebook, or other type computingsystem, and/or one or more peripheral devices) in which all/part ofsystem 100 may be implemented. FIG. 1D illustrates that implementationsof real machine 130 may include all/part of computing device 132 and/orall/part of one or one or more peripherals associated computing device132.

Further, the operation 806 illustrates determining an acceptability ofan effect of content of the data at least in part via at least twovirtual machine representations of a real machine including at least oneperipheral device that is at least one of a printer, a fax machine, aperipheral memory device, a network adapter, a music player, a cellulartelephone, a data acquisition device, or a device actuator. Continuingthe example above, Effect of content acceptability determination engine106 (FIG. 1A) may receive a data content determination from data contentdetermination engine 104 post obtaining of data by data obtainer engine102 and communication of obtained data to data content determinationengine 104. Virtual machine module 118 (FIG. 1A) may spawn at least oneof virtual machines 11, 12, and/or 13 that may be a virtual machinerepresentation of at least a part of real machine 130. Real machine 130may include at least one peripheral device. For instance, FIG. 1Dillustrates a real machine may also include at least a portion of one ormore peripheral devices connected/connectable (e.g., via wired,waveguide, or wireless connections) to real machine 130. Peripheraldevices may include one or more printers 134, one or more fax machines136, one or more peripheral memory devices 138 (e.g., flash drive,memory stick), one or more network adapters 139 (e.g., wired or wirelessnetwork adapters), one or more music players 140, one or more cellulartelephones 142, one or more data acquisition devices 144 (e.g., robots)and/or one or more device actuators 146 (e.g., an hydraulic arm, aradiation emitter, or any other component(s) of industrial/medicalsystems).

FIG. 9 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 9 illustrates example embodiments where theoperation 230 may include at least one additional operation. Additionaloperations may include an operation 902, and/or an operation 904.

The operation 902 illustrates determining a state of at least one of theat least two virtual machine representations operating at least in parton an individual core of a multi-core system prior to loading at least aportion of the data. Continuing the example above, FIG. 1A shows theEffect of content acceptability determination engine 106 including avirtual machine module 118, further including at least one of virtualmachines 11, 12, and/or 13. Upon receiving data and a data contentdetermination from data content determination engine 104 post obtainingof data by data obtainer engine 102, Effect of content acceptabilitydetermination engine 106 may transfer the data and associated datacontent determination to virtual machine module 118. Virtual machinemodule 118 may spawn at least one virtual machine 11, 12, and/or 13 andtransfer the data and associated data content determination to at leastone of virtual machines 11, 12, and/or 13. At least one of virtualmachines 11, 12, and/or 13 (FIG. 1B) may determine a state of at leastone component (e.g., the hardware) of the virtual machine prior toactivation (e.g., before) of a link. Virtual machine state may berepresentative of a state for all or at least a portion of thecomponents (e.g., content, software, hardware, operating system) of thereal machine 130 represented by the virtual machine 11, 12, and/or 13.For instance, at least one of virtual machines 11, 12, and/or 13 may bedetermined to be free of viruses, an amount of virtual machine memorymay be measured, or a processing speed of at least one of virtualmachines 11, 12, and/or 13 may be determined. At least one of virtualmachines 11, 12, and/or 13 may contain a diagnostic applicationconfigured to analyze virtual machine performance and contents.

The operation 904 illustrates determining a state of at least one of theat least two virtual machine representations operating at least in parton an individual core of a multi-core system subsequent to loading atleast a portion of the data. Continuing the example above, FIG. 1A showsthe Effect of content acceptability determination engine 106 including avirtual machine module 118 further including virtual machines 11, 12,and/or 13. Upon receiving data and a data content determination fromdata content determination engine 104 post obtaining of data by dataobtainer engine 102, Effect of content acceptability determinationengine 106 may transfer the data and associated data contentdetermination to virtual machine module 118. Virtual machine module 118may spawn at least one virtual machine 11, 12, and/or 13 and transferthe data and associated data content determination to at least one ofvirtual machines 11, 12, and/or 13. At least one of virtual machines 11,12, and/or 13 (FIG. 1B) may determine a state of at least one component(e.g., the hardware) of the virtual machine subsequent to (e.g., after)activation of a link. For instance a virtual machine state may berepresentative of a state for all characteristics of the real machine130 content, software, hardware or operating system represented by atleast one of virtual machines 11, 12, and/or 13 after at least a portionof the data has loaded. For instance, at least one of virtual machines11, 12, and/or 13 may be determined to contain a virus, an amount ofvirtual machine memory may be measured, or a processing speed of atleast one of virtual machines 11, 12, and/or 13 may be determined. Atleast one of virtual machines 11, 12, and/or 13 may be examined todetermine, for example, if a virus or any other undesired software ispresent on the machine after at least a portion of the data has loadedby examining the virtual machine representation of the operating systemof the real machine 130 (FIG. 1B), or if information from the realmachine 130 has been transferred to an external location by examiningthe software of the real machine 130.

FIG. 10 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 10 illustrates example embodiments where theoperation 230 may include at least one additional operation. Additionaloperations may include an operation 1002, and/or an operation 1004.

The operation 1002 illustrates determining a state change of at leastone of the at least two virtual machine representations operating atleast in part on an individual core of a multi-core system between aprior state and a subsequent state of at least one of the at least twovirtual machine representations operating at least in part on anindividual core of a system comprising at least two cores after loadingat least a portion of the data Continuing the example above, FIG. 1Ashows the Effect of content acceptability determination engine 106including a virtual machine module 118 further including virtualmachines 11, 12, 13. Upon receiving data and a data contentdetermination from data content determination engine 104 post obtainingof data by data obtainer engine 102, Effect of content acceptabilitydetermination engine 106 may transfer the data and associated datacontent determination to virtual machine module 118. Virtual machinemodule 118 may spawn at least one virtual machine 11, 12, and/or 13 andtransfer the data and associated data content determination to at leastone of virtual machines 11, 12, and/or 13. A state change (e.g., adecrease in memory) of at least one of virtual machines 11, 12, and/or13 (FIG. 1B) may be determined by a component of at least one of virtualmachines 11, 12, and/or 13 measuring a characteristic of the virtualmachine representation of the content, software, hardware or operatingsystem of the real machine 130 before and after the at least a portionof data has loaded. For instance, a state change may be measured after asearch result containing a plurality of web links has loaded and atleast one web link has been activated.

Further, the operation 1004 illustrates determining whether a statechange on at least one of the at least two virtual machinerepresentations operating at least in part on an individual core of amulti-core system is an undesirable state change based on one or moreend-user specified preferences. Continuing the example above, FIG. 1Ashows the Effect of content acceptability determination engine 106including a virtual machine module 118 further including virtualmachines 11, 12, 13. Upon receiving data and a data contentdetermination from data content determination engine 104 post obtainingof data by data obtainer engine 102, Effect of content acceptabilitydetermination engine 106 may transfer the data and associated datacontent determination to virtual machine module 118. Virtual machinemodule 118 may spawn at least one virtual machine 11, 12, and/or 13 andtransfer the data and associated data content determination to at leastone of virtual machines 11, 12, and/or 13. An undesirable state changemay be determined by examining the changes to at least one of virtualmachines 11, 12, and/or 13 (FIG. 1B) and comparing the state change ofat least one of virtual machines 11, 12, and/or 13 to user preferencedatabase information spawned on at least one of virtual machines 11, 12,and/or 13 by a transfer of user preference database information from theuser preference database 120 (FIG. 1A) to the virtual machine module 118(FIG. 1A) which spawns a copy of at least a portion of the userpreference database 120 (FIG. 1A) onto at least one of virtual machines11, 12, and/or 13. A state change may include any undesirable statechanges such as a decrease in memory or processing speed and/or thepresence of a virus or other undesirable software after at least aportion of the data has loaded. Undesirable state changes may furtherinclude an undesirable transfer of information located on at least oneof virtual machines 11, 12, and/or 13 to an external location, anundesirable transfer of data onto at least one of virtual machines 11,12, and/or 13 from an external location after at least a portion of thedata has loaded on at least one of virtual machines 11, 12, and/or 13that may result in an undesired change in the state of content,software, hardware or an operating system of the real machine 130 and/oran undesirable transfer of data onto at least one of virtual machines11, 12, and/or 13 where at least a portion of the transferred data maybe found objectionable when viewed by a user 10.

FIG. 11 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 11 illustrates example embodiments where theoperation 230 may include at least one additional operation. Additionaloperations may include an operation 1102, an operation 1104, and/or anoperation 1106.

The operation 1102 illustrates determining an acceptability of an effectof content of the data in response to at least one user setting.Continuing the example above, FIG. 1A shows the Effect of contentacceptability determination engine 106 including a virtual machinemodule 118 further including virtual machines 11, 12, 13. Upon receivingdata and a data content determination from data content determinationengine 104 post obtaining of data by data obtainer engine 102, Effect ofcontent acceptability determination engine 106 may transfer the data andassociated data content determination to virtual machine module 118.Virtual machine module 118 may spawn at least one virtual machine 11,12, and/or 13 and transfer data and associated data contentdetermination to at least one of virtual machines 11, 12, and/or 13. Anacceptability of an effect of content of the data may be determined bydetermining if a state change to at least one of virtual machines 11,12, and/or 13 has occurred and comparing the state change of at leastone of virtual machines 11, 12, and/or 13 to user preference databaseinformation spawned on at least one of virtual machines 11, 12, and/or13. Comparison may be made, for example by transferring user preferencedatabase information from the user preference database 120 (FIG. 1A) tothe virtual machine module 118 (FIG. 1A) which spawns a copy of at leasta portion of the user preference database 120 (FIG. 1A) onto at leastone of virtual machines 11, 12, and/or 13. User preference database 120may include at least one end-user specified preference relating to atleast one of content, software, hardware and/or an operating system of areal machine 130. At least one of virtual machines 11, 12, and/or 13 maydetermine an acceptability of an effect of the content of the data basedon at least one user setting contained in a user preference database atleast a portion of which may be spawned onto at least one of virtualmachines 11, 12, and/or 13 via virtual machine module 118 (e.g., does awebsite contain only images, text, audio or visual data suitable forviewing by a user based on a setting established by a user such as apolitical or cultural preference setting). Further examples of userpreferences include specific religion or lifestyle preference, such as“return only links relating to Roman Catholicism” or “return only linksrelating to a vegan lifestyle” that may be stored in the real machine130. User-specific preference may also relate to user information safetyor computer safety, such as “do not display links requesting informationfrom my computer,” or “do not display links that transfer viruses ontomy computer.”

Further, the operation 1104 illustrates determining an acceptability ofan effect of content of the data in response to a personal user setting.Continuing the example above, FIG. 1A shows the Effect of contentacceptability determination engine 106 including a virtual machinemodule 118 further including virtual machines 11, 12, 13. Upon receivingdata and a data content determination from data content determinationengine 104 post obtaining of data by data obtainer engine 102, Effect ofcontent acceptability determination engine 106 may transfer the data andassociated data content determination to virtual machine module 118.Virtual machine module 118 may spawn at least one virtual machine 11,12, and/or 13 and transfer data and associated data contentdetermination to at least one of virtual machines 11, 12, and/or 13.User preference database information stored in the user preferencedatabase 120 (FIG. 1A) may be transferred to the virtual machine module118 (FIG. 1A), which spawns a copy of at least a portion of the userpreference database 120 (FIG. 1A) onto at least one of virtual machines11, 12, and/or 13. At least one of virtual machines 11, 12, and/or 13(FIG. 1B) may compare the data received from the virtual machine module118 (FIG. 1A) to a personal user setting (e.g., “show only automobilerelated data”) contained in user preference database information spawnedon at least one of virtual machines 11, 12, and/or 13. User preferencedatabase 120 may include at least one personal user setting relating toat least one of content, software, hardware and/or an operating systemof a real machine 130. Personal user setting may be a setting input by auser that is personal to the user, such as an information securitylevel, a content filter level, or a personal desirability setting suchas “show only non-religious data” or “show only automobile relateddata.”

Further, the operation 1106 illustrates determining an acceptability ofan effect of content of the data in response to a peer user setting.Continuing the example above, FIG. 1A shows the Effect of contentacceptability determination engine 106 including a virtual machinemodule 118 further including virtual machines 11, 12, and 13. Uponreceiving data and a data content determination from data contentdetermination engine 104 post obtaining of data by data obtainer engine102, Effect of content acceptability determination engine 106 maytransfer the data and associated data content determination to virtualmachine module 118. Virtual machine module 118 may spawn at least onevirtual machine 11, 12, and/or 13 and transfer data and associated datacontent determination to at least one of virtual machines 11, 12, and/or13. User preference database information stored in the user preferencedatabase 120 (FIG. 1A) may be transferred to the virtual machine module118 (FIG. 1A), which spawns a copy of at least a portion of the userpreference database 120 (FIG. 1A) onto at least one of virtual machines11, 12, and/or 13. At least one of virtual machines 11, 12, and/or 13may compare the data received from the virtual machine module to a peeruser setting contained in user preference database information spawnedon at least one of virtual machines 11, 12, and/or 13. User preferencedatabase 120 may include at least one peer user setting relating to atleast one of content, software, hardware and/or an operating system of areal machine 130. Peer user setting may be a setting input by a userthat is determined by a peer group, such as a peer group determinedinformation security level such as “display only 100 percent securewebsites”, a peer group determined data filter level such as “filter100% of obscene data”, or a peer group desirability setting such as“show only classical music related data” or “show only knitting relateddata.”

FIG. 12 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 12 illustrates example embodiments where theoperation 230 may include at least one additional operation. Additionaloperations may include an operation 1202, and/or an operation 1204.

The operation 1202 illustrates determining an acceptability of an effectof content of the data in response to a corporate user setting.Continuing the example above, FIG. 1A shows the Effect of contentacceptability determination engine 106 including a virtual machinemodule 118 further including virtual machines 11, 12, 13. Upon receivingdata and a data content determination from data content determinationengine 104 post obtaining of data by data obtainer engine 102, Effect ofcontent acceptability determination engine 106 may transfer the data andassociated data content determination to virtual machine module 118.Virtual machine module 118 may spawn at least one virtual machine 11,12, and/or 13 and transfer data and associated data contentdetermination to at least one of virtual machines 11, 12, and/or 13.User preference database information stored in the user preferencedatabase 120 (FIG. 1A) may be transferred to the virtual machine module118 (FIG. 1A), which spawns a copy of at least a portion of the userpreference database 120 (FIG. 1A) onto at least one of virtual machines11, 12, and/or 13. At least one of virtual machines 11, 12, and/or 13(FIG. 1B) may compare the data received from the virtual machine module118 (FIG. 1A) to a corporate user setting contained in user preferencedatabase information spawned on at least one of virtual machines 11, 12,and/or 13. User preference database 120 may include at least onecorporate user setting relating to at least one of content, software,hardware and/or an operating system of a real machine 130. Corporateuser setting may be a setting input by a corporation that is determinedto the corporation, such as a corporate desirability setting such as“show only real-estate related data” or “show only agricultural relateddata.”

The operation 1204 illustrates determining an acceptability of an effectof content of the data in response to a work safety user setting.Continuing the example above, FIG. 1A shows the Effect of contentacceptability determination engine 106 including a virtual machinemodule 118 further including virtual machines 11, 12, 13. Upon receivingdata and a data content determination from data content determinationengine 104 post obtaining of data by data obtainer engine 102, Effect ofcontent acceptability determination engine 106 may transfer the data andassociated data content determination to virtual machine module 118.Virtual machine module 118 may spawn at least one virtual machine 11,12, and/or 13 and transfer data and associated data contentdetermination to at least one of virtual machines 11, 12, and/or 13.User preference database information stored in the user preferencedatabase 120 (FIG. 1A) may be transferred to the virtual machine module118 (FIG. 1A), which spawns a copy of at least a portion of the userpreference database 120 (FIG. 1A) onto at least one of virtual machines11, 12, and/or 13. At least one of virtual machines 11, 12, and/or 13(FIG. 1B) may compare the data received from the virtual machine module118 (FIG. 1A) to a work safety user setting contained in user preferencedatabase information spawned on at least one of virtual machines 11, 12,and/or 13. User preference database 120 may include at least one worksafety user setting relating to at least one of content, software,hardware and/or an operating system of a real machine 130. Thus, in onespecific example, a webpage or website data may be determined to bedisplayable if the data satisfies a work safety user setting such as acorporate information security level, corporate user setting, or acorporate information content filter level corporate user setting.

FIG. 13 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 13 illustrates example embodiments where theoperation 230 may include at least one additional operation. Additionaloperations may include an operation 1302, an operation 1304, and/or anoperation 1306.

The operation 1302 illustrates determining an acceptability of an effectof content of the data in response to a desirability setting. Continuingthe example above, FIG. 1A shows the Effect of content acceptabilitydetermination engine 106 including a virtual machine module 118 furtherincluding virtual machines 11, 12, 13. Upon receiving data and a datacontent determination from data content determination engine 104 postobtaining of data by data obtainer engine 102, Effect of contentacceptability determination engine 106 may transfer the data andassociated data content determination to virtual machine module 118.Virtual machine module 118 may spawn at least one virtual machine 11,12, and/or 13 and transfer data and associated data contentdetermination to at least one of virtual machines 11, 12, and/or 13.User preference database information stored in the user preferencedatabase 120 (FIG. 1A) may be transferred to the virtual machine module118 (FIG. 1A), which spawns a copy of at least a portion of the userpreference database 120 (FIG. 1A) onto at least one of virtual machines11, 12, and/or 13. At least one of virtual machines 11, 12, and/or 13(FIG. 1B) may compare the data received from the virtual machine module118 (FIG. 1A) to a desirability setting (e.g., does a website containonly images, text, audio or visual data suitable for viewing by a userbased on a desirability setting established by a user such as a desireto view only non-obscene material) contained in user preference databaseinformation spawned on at least one of virtual machines 11, 12, and/or13. User preference database 120 may include at least one desirabilitysetting relating to at least one of content, software, hardware and/oran operating system of a real machine 130.

Further, the operation 1304 illustrates determining an acceptability ofan effect of content of the data in response to a religious desirabilitysetting. Continuing the example above, FIG. 1A shows the Effect ofcontent acceptability determination engine 106 including a virtualmachine module 118 further including virtual machines 11, 12, 13. Uponreceiving data and a data content determination from data contentdetermination engine 104 post obtaining of data by data obtainer engine102, Effect of content acceptability determination engine 106 maytransfer the data and associated data content determination to virtualmachine module 118. Virtual machine module 118 may spawn at least onevirtual machine 11, 12, and/or 13 and transfer data and associated datacontent determination to at least one of virtual machines 11, 12, and/or13. User preference database information stored in the user preferencedatabase 120 (FIG. 1A) may be transferred to the virtual machine module118 (FIG. 1A), which spawns a copy of at least a portion of the userpreference database 120 (FIG. 1A) onto at least one of virtual machines11, 12, and/or 13. At least one of virtual machines 11, 12, and/or 13may compare the data received from the virtual machine module 118 to areligious desirability setting (e.g., does a website contain onlyimages, text, audio or visual data suitable for viewing by a user basedon a religious desirability setting established by a user such as adesire to view only Hindu material) contained in user preferencedatabase information spawned on at least one of virtual machines 11, 12,and/or 13. A religious desirability setting may be include any settingregarding a major, minor, or other religion such as Christianity,Judaism, Islam, Hinduism, and so on.

Further, the operation 1306 illustrates determining an acceptability ofan effect of content of the data in response to a political desirabilitysetting. Continuing the example above, FIG. 1A shows the Effect ofcontent acceptability determination engine 106 including a virtualmachine module 118 further including virtual machines 11, 12, 13. Uponreceiving data and a data content determination from data contentdetermination engine 104 post obtaining of data by data obtainer engine102, Effect of content acceptability determination engine 106 maytransfer the data and associated data content determination to virtualmachine module 118. Virtual machine module 118 may spawn at least onevirtual machine 11, 12, and/or 13 and transfer data and associated datacontent determination to at least one of virtual machines 11, 12, and/or13. User preference database information stored in the user preferencedatabase 120 (FIG. 1A) may be transferred to the virtual machine module118 (FIG. 1A), which spawns a copy of at least a portion of the userpreference database 120 (FIG. 1A) onto at least one of virtual machines11, 12, and/or 13. At least one of virtual machines 11, 12, and/or 13(FIG. 1B) may compare the data received from the virtual machine module118 (FIG. 1A) to a political desirability setting (e.g., does a websitecontain only images, text, audio or visual data suitable for viewing bya user based on a political desirability setting established by a usersuch as a desire to view only Democratic Party material) contained inuser preference database information spawned on at least one of virtualmachines 11, 12, and/or 13. A political desirability setting may includeany setting regarding a political party or affiliation (e.g.,Republican, Democratic, Libertarian, Green Party, etc.).

FIG. 14 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 14 illustrates example embodiments where theoperation 230 may include at least one additional operation. Additionaloperations may include an operation 1402, and/or an operation 1404.

The operation 1402 illustrates determining an acceptability of an effectof content of the data in response to a cultural desirability setting.Continuing the example above, FIG. 1A shows the Effect of contentacceptability determination engine 106 including a virtual machinemodule 118 further including virtual machines 11, 12, 13. Upon receivingdata and a data content determination from data content determinationengine 104 post obtaining of data by data obtainer engine 102, Effect ofcontent acceptability determination engine 106 may transfer the data andassociated data content determination to virtual machine module 118.Virtual machine module 118 may spawn at least one virtual machine 11,12, and/or 13 and transfer data and associated data contentdetermination to at least one of virtual machines 11, 12, and/or 13.User preference database information stored in the user preferencedatabase 120 (FIG. 1A) may be transferred to the virtual machine module118 (FIG. 1A), which spawns a copy of at least a portion of the userpreference database 120 (FIG. 1A) onto at least one of virtual machines11, 12, and/or 13. At least one of virtual machines 11, 12, and/or 13(FIG. 1B) may compare the data received from the virtual machine module118 (FIG. 1A) to a cultural desirability setting (e.g., does a websitecontain only images, text, audio or visual data suitable for viewing bya user based on a cultural desirability setting established by a usersuch as a desire to view only materials regarding early Mayancivilization) contained in user preference database information spawnedon at least one of virtual machines 11, 12, and/or 13. A culturaldesirability setting may include any culturally related information suchas a religious, ethnic, regional, or heritage based culturaldesirability setting or any other cultural desirability setting.

Further, the operation 1404 illustrates determining an acceptability ofan effect of content of the data in response to a theme relateddesirability setting. Continuing the example above, FIG. 1A shows theEffect of content acceptability determination engine 106 including avirtual machine module 118 further including virtual machines 11, 12,13. Upon receiving data and a data content determination from datacontent determination engine 104 post obtaining of data by data obtainerengine 102, Effect of content acceptability determination engine 106 maytransfer the data and associated data content determination to virtualmachine module 118. Virtual machine module 118 may spawn at least onevirtual machine 11, 12, and/or 13 and transfer data and associated datacontent determination to at least one of virtual machines 11, 12, and/or13. User preference database information stored in the user preferencedatabase 120 (FIG. 1A) may be transferred to the virtual machine module118 (FIG. 1A), which spawns a copy of at least a portion of the userpreference database 120 (FIG. 1A) onto at least one of virtual machines11, 12, and/or 13. At least one of virtual machines 11, 12, and/or 13(FIG. 1B) may compare the data received from the virtual machine module118 (FIG. 1A) to a theme related desirability setting (e.g., does awebsite contain only images, text, audio or visual data suitable forviewing by a user based on a theme related desirability settingestablished by a user such as a desire to view only materials regardingcollectible stamps) contained in user preference database informationspawned on at least one of virtual machines 11, 12, and/or 13. A themerelated desirability setting may include any theme related information,such as information relating to cars, fashion, electronics, sports,hobbies, collector's items, or any theme or category that may be ofinterest to a user.

FIG. 15 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 15 illustrates example embodiments where theoperation 230 may include at least one additional operation. Additionaloperations may include an operation 1502.

The operation 1502 illustrates determining an acceptability of an effectof content of the data in response to an age appropriatenessdesirability setting. Continuing the example above, FIG. 1A shows theEffect of content acceptability determination engine 106 including avirtual machine module 118 further including virtual machines 11, 12,13. Upon receiving data and a data content determination from datacontent determination engine 104 post obtaining of data by data obtainerengine 102, Effect of content acceptability determination engine 106 maytransfer the data and associated data content determination to virtualmachine module 118. Virtual machine module 118 may spawn at least onevirtual machine 11, 12, and/or 13 and transfer data and associated datacontent determination to at least one of virtual machines 11, 12, and/or13. User preference database information stored in the user preferencedatabase 120 (FIG. 1A) may be transferred to the virtual machine module118 (FIG. 1A), which spawns a copy of at least a portion of the userpreference database 120 (FIG. 1A) onto at least one of virtual machines11, 12, and/or 13. Atat least one of virtual machines 11, 12, and/or 13(FIG. 1B) may compare the data received from the virtual machine module118 (FIG. 1A) to an age appropriateness desirability setting (e.g., doesa website contain only images, text, audio or visual data suitable forviewing by a user based on an age appropriateness desirability settingestablished by a user such as a desire to view only materials given a PGor lower rating as determined by the Motion Picture of AmericaAssociation film rating system) contained in user preference databaseinformation spawned on at least one of virtual machines 11, 12, and/or13. An age appropriateness desirability setting may include any ageappropriate setting, such as a rating threshold or a profanitythreshold.

FIG. 16 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 16 illustrates example embodiments where theoperation 230 may include at least one additional operation. Additionaloperations may include an operation 1602, an operation 1604, and/or anoperation 1606.

The operation 1602 illustrates determining an acceptability of an effectof content of the data in response to at least one privacy relatedsetting. Continuing the example above, FIG. 1A shows the Effect ofcontent acceptability determination engine 106 including a virtualmachine module 118 further including virtual machines 11, 12, 13. Uponreceiving data and a data content determination from data contentdetermination engine 104 post obtaining of data by data obtainer engine102, Effect of content acceptability determination engine 106 maytransfer the data and associated data content determination to virtualmachine module 118. Virtual machine module 118 may spawn at least onevirtual machine 11, 12, and/or 13 and transfer data and associated datacontent determination to at least one of virtual machines 11, 12, and/or13. User preference database information stored in the user preferencedatabase 120 (FIG. 1A) may be transferred to the virtual machine module118 (FIG. 1A), which spawns a copy of at least a portion of the userpreference database 120 (FIG. 1A) onto at least one of virtual machines11, 12, and/or 13. At least one of virtual machines 11, 12, and/or 13(FIG. 1B) may compare the data received from the virtual machine module118 (FIG. 1A) to a privacy related setting (e.g., does a website containonly images, text, audio or visual data suitable for viewing by a userbased on a privacy related setting established by a user) contained inuser preference database information spawned on at least one of virtualmachines 11, 12, and/or 13. A privacy related setting may include anyprivacy related settings (e.g., does a website contain only data thatwill not request information from my computer or allow others to viewpersonal information saved on my computer).

Further, the operation 1604 illustrates determining an acceptability ofan effect of content of the data in response to a user specific privacyrelated setting. Continuing the example above, FIG. 1A shows the Effectof content acceptability determination engine 106 including a virtualmachine module 118 further including virtual machines 11, 12, 13. Uponreceiving data and a data content determination from data contentdetermination engine 104 post obtaining of data by data obtainer engine102, Effect of content acceptability determination engine 106 maytransfer the data and associated data content determination to virtualmachine module 118. Virtual machine module 118 may spawn at least onevirtual machine 11, 12, and/or 13 and transfer data and associated datacontent determination to at least one of virtual machines 11, 12, and/or13. User preference database information stored in the user preferencedatabase 120 (FIG. 1A) may be transferred to the virtual machine module118 (FIG. 1A), which spawns a copy of at least a portion of the userpreference database 120 (FIG. 1A) onto at least one of virtual machines11, 12, and/or 13. At least one of virtual machines 11, 12, and/or 13(FIG. 1B) may compare the data received from the virtual machine module118 (FIG. 1A) to a user specific privacy related setting (e.g., will awebsite request specific information about the user such as name,address, telephone number) contained in user preference databaseinformation spawned on at least one of virtual machines 11, 12, and/or13. A user specific privacy related setting may include any userspecific privacy related settings (e.g., a setting relating to a user'sbiographical information or financial information).

Further, the operation 1606 illustrates determining an acceptability ofan effect of content of the data in response to a group privacy relatedsetting. Continuing the example above, FIG. 1A shows the Effect ofcontent acceptability determination engine 106 including a virtualmachine module 118 further including virtual machines 11, 12, 13. Uponreceiving data and a data content determination from data contentdetermination engine 104 post obtaining of data by data obtainer engine102, Effect of content acceptability determination engine 106 maytransfer the data and associated data content determination to virtualmachine module 118. Virtual machine module 118 may spawn at least onevirtual machine 11, 12, and/or 13 and transfer data and associated datacontent determination to at least one of virtual machines 11, 12, and/or13. User preference database information stored in the user preferencedatabase 120 (FIG. 1A) may be transferred to the virtual machine module118 (FIG. 1A), which spawns a copy of at least a portion of the userpreference database 120 (FIG. 1A) onto at least one of virtual machines11, 12, and/or 13. At least one of virtual machines 11, 12, and/or 13(FIG. 1B) may compare the data received from the virtual machine module118 (FIG. 1A) to a group privacy related setting (e.g., will a websiterequest information about an organization such as name, address,telephone number) contained in user preference database informationspawned on at least one of virtual machines 11, 12, and/or 13. A groupprivacy related setting may include any group privacy related settings(e.g., a setting relating to a group's membership). Group privacyrelated setting may be any setting established by a group such as a workgroup (e.g., employees of a company), a peer group (e.g., members of abook club), or a family group (e.g., members of family unit) privacyrelated setting.

FIG. 17 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 17 illustrates example embodiments where theoperation 230 may include at least one additional operation. Additionaloperations may include an operation 1702, and/or an operation 1704.

The operation 1702 illustrates determining an acceptability of an effectof content of the data in response to a corporate privacy relatedsetting. Continuing the example above, FIG. 1A shows the Effect ofcontent acceptability determination engine 106 including a virtualmachine module 118 further including virtual machines 11, 12, 13. Uponreceiving data and a data content determination from data contentdetermination engine 104 post obtaining of data by data obtainer engine102, Effect of content acceptability determination engine 106 maytransfer the data and associated data content determination to virtualmachine module 118. Virtual machine module 118 may spawn at least onevirtual machine 11, 12, and/or 13 and transfer data and associated datacontent determination to at least one of virtual machines 11, 12, and/or13. User preference database information stored in the user preferencedatabase 120 (FIG. 1A) may be transferred to the virtual machine module118 (FIG. 1A), which spawns a copy of at least a portion of the userpreference database 120 (FIG. 1A) onto at least one of virtual machines11, 12, and/or 13. At least one of virtual machines 11, 12, and/or 13(FIG. 1B) may compare the data received from the virtual machine module118 (FIG. 1A) to a corporate privacy related setting (e.g., will awebsite request information about a corporation such as data stored on areal machine belonging to the corporation) contained in user preferencedatabase information spawned on at least one of virtual machines 11, 12,and/or 13. Corporate privacy related setting may be determined by acorporate issued privacy manual, or other such document or mandate setforth by officers of a corporation.

Further, the operation 1704 illustrates determining an acceptability ofan effect of content of the data in response to transmitted userinformation. Continuing the example above, FIG. 1A shows the Effect ofcontent acceptability determination engine 106 including a virtualmachine module 118 further including virtual machines 11, 12, 13. Uponreceiving data and a data content determination from data contentdetermination engine 104 post obtaining of data by data obtainer engine102, Effect of content acceptability determination engine 106 maytransfer the data and associated data content determination to virtualmachine module 118. Virtual machine module 118 may spawn at least onevirtual machine 11, 12, and/or 13 and transfer data and associated datacontent determination to at least one of virtual machines 11, 12, and/or13. User preference database information stored in the user preferencedatabase 120 (FIG. 1A) may be transferred to the virtual machine module118 (FIG. 1A), which spawns a copy of at least a portion of the userpreference database 120 (FIG. 1A) onto at least one of virtual machines11, 12, and/or 13. At least one of virtual machines 11, 12, and/or 13(FIG. 1B) may compare the data received from the virtual machine module118 (FIG. 1A) to at least one acceptable type of transmitted userinformation setting (e.g., do not return links that will transmit mye-mail address, home address or telephone number to an externallocation) contained in user preference database information spawned onat least one of virtual machines 11, 12, and/or 13. Acceptable type oftransmitted user information setting may be determined by a user 10(FIG. 1B). For instance, acceptability of the effect of the data may bedetermined in response to whether or not private user information, suchas credit card numbers, bank accounts, personal identificationinformation or any other personal user information may be transmitted toa location external to the real machine by selecting the link.

FIG. 18 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 18 illustrates example embodiments where theoperation 230 may include at least one additional operation. Additionaloperations may include an operation 1802, and/or an operation 1804.

The operation 1802 illustrates determining an acceptability of an effectof content of the data in response to captured user information.Continuing the example above, FIG. 1A shows the Effect of contentacceptability determination engine 106 including a virtual machinemodule 118 further including virtual machines 11, 12, 13. Upon receivingdata and a data content determination from data content determinationengine 104 post obtaining of data by data obtainer engine 102, Effect ofcontent acceptability determination engine 106 may transfer the data andassociated data content determination to virtual machine module 118.Virtual machine module 118 may spawn at least one virtual machine 11,12, and/or 13 and transfer data and associated data contentdetermination to at least one of virtual machines 11, 12, and/or 13.User preference database information stored in the user preferencedatabase 120 (FIG. 1A) may be transferred to the virtual machine module118 (FIG. 1A), which spawns a copy of at least a portion of the userpreference database 120 (FIG. 1A) onto at least one of virtual machines11, 12, and/or 13. At least one of vial machines 11, 12, and/or 13 (FIG.1B) may compare the data received from the virtual machine module 118(FIG. 1A) to at least one acceptable type of captured user informationsetting (e.g., do not return links that will capture my e-mail address,home address or telephone number) contained in user preference databaseinformation spawned on at least one of virtual machines 11, 12, and/or13. Acceptable type of captured user information setting may bedetermined by a user 10 (FIG. 1B). For instance, acceptability of theeffect of the data may be determined in response to whether or notprivate user information, such as credit card numbers, bank accounts,personal identification information or any other personal userinformation may be captured by a machine located at a location externalto the real machine by selecting the link.

Further, the operation 1804 illustrates determining an acceptability ofan effect of content of the data in response to exposed userinformation. Continuing the example above, FIG. 1A shows the Effect ofcontent acceptability determination engine 106 including a virtualmachine module 118 further including virtual machines 11, 12, 13. Uponreceiving data and a data content determination from data contentdetermination engine 104 post obtaining of data by data obtainer engine102, Effect of content acceptability determination engine 106 maytransfer the data and associated data content determination to virtualmachine module 118. Virtual machine module 118 may spawn at least onevirtual machine 11, 12, and/or 13 and transfer data and associated datacontent determination to at least one of virtual machines 11, 12, and/or13. User preference database information stored in the user preferencedatabase 120 (FIG. 1A) may be transferred to the virtual machine module118 (FIG. 1A), which spawns a copy of at least a portion of the userpreference database 120 (FIG. 1A) onto at least one of virtual machines11, 12, and/or 13. At least one of virtual machines 11, 12, and/or 13(FIG. 1B) may compare the data received from the virtual machine module118 (FIG. 1A) to at least one acceptable type of exposed userinformation setting (e.g., do not return links that will expose personalfinancial information stored on the real machine 130) contained in userpreference database information spawned on at least one of virtualmachines 11, 12, and/or 13. Acceptable types of exposed user informationsettings may be determined by a user 10 (FIG. 1B). For instance,acceptability of the effect of the data may be determined in response towhether or not private user information, such as credit card numbers,bank accounts, personal identification information or any other personaluser information may be exposed to a machine located at a locationexternal to the real machine by selecting the link.

FIG. 19 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 19 illustrates example embodiments where theoperation 230 may include at least one additional operation. Additionaloperations may include an operation 1902, and/or an operation 1904.

The operation 1902 illustrates determining an acceptability of an effectof content of the data in response to visually examining at least aportion of a data image on at least one of the at least two virtualmachine representations operating at least in part on an individual coreof a multi-core system. Continuing the example above, FIG. 1A shows theEffect of content acceptability determination engine 106 including avirtual machine module 118 further including virtual machines 11, 12,13. Upon receiving data and a data content determination from datacontent determination engine 104 post obtaining of data by data obtainerengine 102, Effect of content acceptability determination engine 106 maytransfer the data and associated data content determination to virtualmachine module 118. Virtual machine module 118 may spawn at least onevirtual machine 11, 12, and/or 13 and transfer data and associated datacontent determination to at least one of virtual machines 11, 12, and/or13. To visually examine a data image, at least one of virtual machines11, 12, and/or 13 (FIG. 1B) may include an image scanning module.Visually examining the data image may include, for example, coloranalysis, pattern-matching, pattern-recognition, or any other techniquefor recognizing a particular image or type of image.

The operation 1904 illustrates determining an acceptability of an effectof content of the data at least in part via at least three virtualmachine representations of at least a part of a real machine having atleast one end-user specified preference, at least one of the at leastthree virtual machine representations operating at least in part on anindividual core of a multi-core system comprising at least three cores(i.e. chip-level multiprocessor). Continuing the example above, FIG. 1Ashows the Effect of content acceptability determination engine 106including a virtual machine module 118, further including virtualmachines 11, 12, and/or 13 operating on Cores 11, 12, and/or 13,respectively of a multi-core system. Upon receiving data and a datacontent determination from data content determination engine 104 postobtaining of data by data obtainer engine 102, Effect of contentacceptability determination engine 106 may transfer the data andassociated data content determination to virtual machine module 118.Virtual machine module 118 may spawn at least one virtual machine 11,12, and/or 13 and transfer the data and associated data contentdetermination to at least one of virtual machines 11, 12, and/or 13. Atleast one of virtual machines 11, 12, and/or 13 may determine a state ofat least one component (e.g., the hardware) of the virtual machine priorto activation (e.g., before) of a link. Virtual machine state may berepresentative of a state for all or at least a portion of thecomponents (e.g., content, software, hardware, operating system) of thereal machine 130 represented by the virtual machine 11, 12, and/or 13.Multi-core system may include at least one additional core, such as, forinstance, Core 31 (FIG. 1C), Core 32 (FIG. 1C) and/or Core 33 (FIG. 1C).

FIG. 20 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 20 illustrates example embodiments where theoperation 240 may include at least one additional operation. Additionaloperations may include an operation 2002, and/or an operation 2004.

The operation 2002 illustrates providing a data display option ofdisplaying at least a portion of the data. Continuing the example above,data provider engine 108 (FIG. 1A) may be in communication with Effectof content acceptability determination engine 106 (FIG. 1A), which mayreceive data and an associated data content determination (e.g., data isa video file) from data content determination engine 104 (FIG. 1A) postobtaining of data by data obtainer engine 102 (FIG. 1A). Effect ofcontent acceptability determination engine 106 may transfer effect ofcontent acceptability determination to the data provider engine 108 toprovide the data display option of displaying at least a portion of thedata. For instance, data content provider engine 108 may receive atleast one display instruction (e.g., OK to display the entire text oflink 1) from at least one component of Effect of content acceptabilitydetermination engine 106 (FIG. 1A). At least one of virtual machines 11,12, and/or 13 may include one or more instruction generating modulesconfigured to provide an instruction to the Effect of contentacceptability determination engine 106 after a comparison of anactivation of a link to a user preference stored in a copy of the userpreference database 120 (FIG. 1A) spawned on the virtual machine 11, 12,13. Effect of content acceptability determination engine 106 maycommunicate the display instruction to the data content provider engine108. Data content provider engine 108 may then display the data.Displayed data may be an unmodified web page of text, images and/orvideo, or a web page including links to additional web pages and may bedisplayed on a real machine display such as a computer screen.

The operation 2004 illustrates providing a data display option of notdisplaying at least a portion of the data. Continuing the example above,data provider engine 108 (FIG. 1A) may be in communication with Effectof content acceptability determination engine 106 (FIG. 1A), which mayreceive data and an associated data content determination (e.g., data isa video file) from data content determination engine 104 (FIG. 1A) postobtaining of data by data obtainer engine 102 (FIG. 1A). Effect ofcontent acceptability determination engine 106 may transfer effect ofcontent acceptability determination to the data provider engine 108 toprovide the data display option of not displaying at least a portion ofthe data. For instance, data content provider engine 108 may receive atleast one do not display instruction (e.g., Do not display the text oflink 1) from at least one component of Effect of content acceptabilitydetermination engine 106 (FIG. 1A). At least one of virtual machines 11,12, and/or 13 (FIG. 1B) may include one or more instruction generatingmodules configured to provide a do not display instruction to the Effectof content acceptability determination engine 106 after a comparison ofan activation of a link to a user preference stored in a copy of theuser preference database 120 (FIG. 1A) spawned on the virtual machine11, 12, 13. Effect of content acceptability determination engine 106 maycommunicate the do not display instruction to the data content providerengine 108. The data display option of not displaying the data mayinclude a message indicated why the data is not being displayed, or maybe, for example, a blank page displayed on a display of the realmachine.

FIG. 21 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 21 illustrates example embodiments where theoperation 240 may include at least one additional operation. Additionaloperations may include an operation 2102, an operation 2104, and/or anoperation 2106.

The operation 2102 illustrates providing a data display option ofdisplaying a modified version of the data. Continuing the example above,data provider engine 108 (FIG. 1A) may be in communication with Effectof content acceptability determination engine 106 (FIG. 1A), which mayreceive data and an associated data content determination (e.g., data isa video file) from data content determination engine 104 (FIG. 1A) postobtaining of data by data obtainer engine 102 (FIG. 1A). Effect ofcontent acceptability determination engine 106 may transfer effect ofcontent acceptability determination to the data provider engine 108 toprovide the data display option of displaying at least a portion of thedata. For instance, data content provider engine 108 (FIG. 1A) mayreceive at least one modify data instruction (e.g., display only lines1-10 of the text of link 1) from at least one component of Effect ofcontent acceptability determination engine 106 (FIG. 1A). At least oneof virtual machines 11, 12, and/or 13 may include one or moreinstruction generating modules configured to provide a modify datainstruction to the Effect of content acceptability determination engine106 after a comparison of an activation of a link to a user preferencestored in a copy of the user preference database 120 (FIG. 1A) spawnedon the virtual machine 11, 12, 13. Effect of content acceptabilitydetermination engine 106 may communicate the modify data instruction tothe data content provider engine 108. The data content provider engine108 may transmit the modify data instruction to the data modificationengine 122 for modification of the data. Data modification engine maytransmit the modified data to the data content provider engine 108. Datacontent provider engine 108 may then display the modified version of thedata. Displayed data may be a modified web page of text, a modifiedimage and/or a modified video, or a modified web page including links toadditional web pages. For instance, a webpage or website may bedisplaying, but any obscenities on the web page or website may replacedby non-obscene word alternatives.

Further, the operation 2104 illustrates providing a data display optionof obfuscating an objectionable data portion. Continuing the exampleabove, data provider engine 108 (FIG. 1A) may be in communication withEffect of content acceptability determination engine 106 (FIG. 1A),which may receive data and an associated data content determination(e.g., data is a video file) from data content determination engine 104(FIG. 1A) post obtaining of data by data obtainer engine 102 (FIG. 1A).Effect of content acceptability determination engine 106 may transfereffect of content acceptability determination to the data providerengine 108 to provide the data display option of obfuscating (e.g.,blurring) a portion of the data (e.g., obscene photos). For instance,data content provider engine 108 may receive at least one obfuscate datainstruction (e.g., display only non-obscene portions of the image inlink 1) from at least one component of Effect of content acceptabilitydetermination engine 106 (FIG. 1A). At least one of virtual machines 11,12, and/or 13 (FIG. 1B) may include one or more instruction generatingmodules configured to provide an obfuscate data instruction to theEffect of content acceptability determination engine 106 after acomparison of an activation of a link to a user preference stored in acopy of the user preference database 120 (FIG. 1A) spawned on thevirtual machine 11, 12, 13. Effect of content acceptabilitydetermination engine 106 may communicate the obfuscate data instructionto the data content provider engine 108. The data content providerengine 108 may transmit the obfuscate data instruction to the datamodification engine 122 which may transmit the obfuscate datainstruction to the data obfuscation engine 124. Data obfuscation engine124 may transmit the obfuscated data to the data modification engine 122for transmission to the data content provider engine 108. Data contentprovider engine 108 may then display the obfuscated version of the data.For example, obfuscating logic may obfuscate restricted data or imagerywithin a webpage or image. Obfuscation may include blurring or blockingof the objectionable data portion.

Further, the operation 2106 illustrates providing a data display optionof anonymizing an objectionable data portion. Continuing the exampleabove, data provider engine 108 (FIG. 1A) may be in communication withEffect of content acceptability determination engine 106 (FIG. 1A),which may receive data and an associated data content determination(e.g., data entails a video file of wmv format) from data contentdetermination engine 104 (FIG. 1A) post obtaining of data by dataobtainer engine 102 (FIG. 1A). Effect of content acceptabilitydetermination engine 106 may transfer effect of content acceptabilitydetermination and an instruction to the data provider engine 108 toprovide the data display option of anonymizing (e.g., obscuring sourceinformation) for a portion of the data (e.g., graphic videos). Forinstance, data content provider engine 108 may receive at least oneanonymize data instruction (e.g., obscure source information forportions of the video in link 1) from at least one component of Effectof content acceptability determination engine 106 (FIG. 1A). At leastone of virtual machines 11, 12, and/or 13 may include one or moreinstruction generating modules configured to provide an anonymize datainstruction to the Effect of content acceptability determination engine106 after a comparison of an activation of a link to a user preferencestored in a copy of the user preference database 120 (FIG. 1A) spawnedon the virtual machine 11, 12, 13. Effect of content acceptabilitydetermination engine 106 may communicate the anonymize data instructionto the data content provider engine 108. The data content providerengine 108 may transmit the anonymize data instruction to the datamodification engine 122 which may transmit the anonymize datainstruction to the data anonymization engine 126. Data anonymizationengine 126 may transmit the anonymized data to the data modificationengine 122 for transmission to the data content provider engine 108.Data content provider engine 108 may then display the anonymized versionof the data. Anonymized data may be data in which the original identityinformation of the data is hidden, obscured, replaced, and/or otherwisemodified.

FIG. 22 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 22 illustrates example embodiments where theoperation 240 may include at least one additional operation. Additionaloperations may include an operation 2202.

The operation 2202 illustrates providing a data display option of atleast one of removing, altering or replacing an objectionable dataportion. Continuing the example above, data provider engine 108 (FIG.1A) may be in communication with Effect of content acceptabilitydetermination engine 106 (FIG. 1A), which may receive data and anassociated data content determination (e.g., data entails an audio fileof MP3 format) from data content determination engine 104 (FIG. 1A) postobtaining of data by data obtainer engine 102 (FIG. 1A). Effect ofcontent acceptability determination engine 106 may transfer effect ofcontent acceptability determination and an instruction to the dataprovider engine 108 to provide the data display option of removing,altering or replacing an objectionable data portion (e.g., replacingprofanity with innocuous language) for a portion of the data (e.g.,explicit lyrics). For instance, data content provider engine 108 mayreceive at least one alter, remove or replace instruction (e.g., obscuresource information for portions of the video in link 1) from at leastone component of Effect of content acceptability determination engine106 (FIG. 1A). At least one of virtual machines 11, 12, and/or 13 mayinclude one or more instruction generating modules configured to providea remove, alter or replace data instruction to the Effect of contentacceptability determination engine 106 after a comparison of anactivation of a link to a user preference stored in a copy of the userpreference database 120 (FIG. 1A) spawned on the virtual machine 11, 12,13. Effect of content acceptability determination engine 106 maycommunicate the remove, alter or replace data instruction to the datacontent provider engine 108. The data content provider engine 108 maytransmit the anonymize data instruction to the data modification engine122 which may then remove, alter or replace the data. Data modificationengine 122 may transmit the data containing removed, altered or replacedportions to the data content provider engine 108. Data content providerengine 108 may then display the data containing removed, altered, orreplaced portions. Thus, in one specific example, a portion of a webpageproduced by a search including data relating to religions other thanCatholicism may be removed from the web page prior to display of thedata on a real machine display such as a computer screen.

FIG. 23 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 23 illustrates example embodiments where theoperation 240 may include at least one additional operation. Additionaloperations may include an operation 2302, and/or an operation 2304.

The operation 2302 illustrates providing a data display option ofdisplaying a data portion consistent with at least one user-relatedsetting. Continuing the example above, data provider engine 108 (FIG.1A) may be in communication with Effect of content acceptabilitydetermination engine 106 (FIG. 1A), which may receive data and anassociated data content determination from data content determinationengine 104 (FIG. 1A) post obtaining of data by data obtainer engine 102(FIG. 1A). Effect of content acceptability determination engine 106 maytransfer effect of content acceptability determination to the dataprovider engine 108 to provide the data display option of displayingdata consistent with at least one user setting. For instance, datacontent provider engine 108 may receive at least one display instruction(e.g., OK to display webpage) from at least one component of Effect ofcontent acceptability determination engine 106 (FIG. 1A). At least oneof virtual machines 11, 12, and/or 13 may include one or moreinstruction generating modules configured to provide an instruction tothe Effect of content acceptability determination engine 106 after acomparison of an activation of a link to a user setting stored in a copyof the user preference database 120 (FIG. 1A) spawned on the virtualmachine 11, 12, 13. Effect of content acceptability determination engine106 may communicate the display instruction to the data content providerengine 108. If displayed data needs to be modified to be consistent withat least one user setting, the data content provider engine 108 (FIG.1A) may transmit the modify data instruction to the data modificationengine 122 for modification of the data. Data modification engine 122(FIG. 1A) may transmit the modified data to the data content providerengine 108. Data content provider engine 108 may then display the dataconsistent with the user setting. Thus, a webpage or website data may bedetermined to be displayable if the data satisfies a user setting whenat least one of virtual machines 11, 12, and/or 13 compares the data tothe user setting. For instance, a portion of a webpage produced by asearch including non-English text may be removed from the web page priorto display of the data on a computer screen. Further, in one specificexample, a webpage or website data may be determined to be displayableif the data satisfies a peer user setting, or a webpage or website datamay be determined to be displayable if the data satisfies, for instance,a corporate user setting.

Further, the operation 2304 illustrates providing a data display optionof displaying a data portion consistent with a privacy related usersetting. Continuing the example above, data provider engine 108 (FIG.1A) may be in communication with Effect of content acceptabilitydetermination engine 106 (FIG. 1A), which may receive data and anassociated data content determination from data content determinationengine 104 (FIG. 1A) post obtaining of data by data obtainer engine 102(FIG. 1A). Effect of content acceptability determination engine 106 maytransfer effect of content acceptability determination to the dataprovider engine 108 to provide the data display option of displayingdata consistent with at least one privacy related setting. For instance,data content provider engine 108 may receive at least one displayinstruction (e.g., OK to display webpage) from at least one component ofEffect of content acceptability determination engine 106 (FIG. 1A). Atleast one of virtual machines 11, 12, and/or 13 may include one or moreinstruction generating modules configured to provide an instruction tothe Effect of content acceptability determination engine 106 after acomparison of an activation of a link to a privacy related settingstored in a copy of the user preference database 120 (FIG. 1A) spawnedon the virtual machine 11, 12, 13. Effect of content acceptabilitydetermination engine 106 may communicate the display instruction to thedata content provider engine 108. If displayed data needs to be modifiedto be consistent with at least one privacy related setting, the datacontent provider engine 108 (FIG. 1A) may transmit the modify datainstruction to the data modification engine 122 (FIG. 1A) formodification of the data. Data modification engine 122 may transmit themodified data to the data content provider engine 108. Data contentprovider engine 108 may then display the data consistent with theprivacy related setting. For instance, a portion of a returned webpageincluding data requesting private user information such as a user'ssocial security number or e-mail address may be removed from the webpage prior to display of the data on a computer screen. Further specificexamples include a webpage or website data may be determined to bedisplayable if the data satisfies a setting such as a privacy relatedsetting such as a setting relating to a user's biographical informationor financial information, a webpage or website data may be determined tobe displayable if the data satisfies a group privacy related settingsuch as a work group (e.g., employees of a company), a peer group (e.g.,members of a book club), or a family group (e.g., members of familyunit) privacy related setting, or a webpage or website data may bedetermined to be displayable if the data satisfies a privacy settingdetermined by a corporation or other organization to maintain corporateor organization privacy.

FIG. 24 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 24 illustrates example embodiments where theoperation 240 may include at least one additional operation. Additionaloperations may include an operation 2402.

The operation 2402 illustrates providing a data display option ofdisplaying a data portion consistent with a desirability setting.Continuing the example above, data provider engine 108 (FIG. 1A) may bein communication with Effect of content acceptability determinationengine 106 (FIG. 1A), which may receive data and an associated datacontent determination (e.g., data entails an image of format JPEG) fromdata content determination engine 104 (FIG. 1A) post obtaining of databy data obtainer engine 102 (FIG. 1A). Effect of content acceptabilitydetermination engine 106 may transfer effect of content acceptabilitydetermination to the data provider engine 108 to provide the datadisplay option of displaying data consistent with at least onedesirability setting. For instance, data content provider engine 108 mayreceive at least one display instruction (e.g., OK to display image)from at least one component of Effect of content acceptabilitydetermination engine 106 (FIG. 1A). At least one of virtual machines 11,12, and/or 13 may include one or more instruction generating modulesconfigured to provide an instruction to the Effect of contentacceptability determination engine 106 after a comparison of anactivation of a link to a desirability setting stored in a copy of theuser preference database 120 (FIG. 1A) spawned on the virtual machine11, 12, 13. Effect of content acceptability determination engine 106 maycommunicate the display instruction to the data content provider engine108. If displayed data needs to be modified to be consistent with atleast one desirability setting, the data content provider engine 108(FIG. 1A) may transmit the modify data instruction to the datamodification engine 122 (FIG. 1A) for modification of the data. Datamodification engine 122 may transmit the modified data to the datacontent provider engine 108. Data content provider engine 108 may thendisplay the data portion consistent with the desirability setting. Forinstance, the data display option may be displaying on a display of areal machine only a data portion consistent with a Christiandesirability setting such as “display only Christianity related data.”In other examples, a webpage or website data may be determined to bedisplayable if the data satisfies a desirability setting, a webpage orwebsite data may be determined to be displayable if the data satisfies areligious desirability setting such as a Christian, Jewish, and/orMuslim, based religious desirability setting, or may be based on anyother major, minor or alternative religious desirability setting, awebpage or website data may be determined to be displayable if the datasatisfies a political desirability setting such as a Republican,Democratic, Libertarian or Green Party political desirability setting, awebpage or website data may be determined to be displayable if the datasatisfies a cultural desirability setting such as a religious, ethnic,regional, or heritage based cultural desirability setting or any othercultural desirability setting, a webpage or website data may bedetermined to be displayable if the data satisfies a theme relateddesirability setting such as boating or card games, or a webpage orwebsite data may be determined to be displayable if the data satisfiesan age appropriateness desirability setting such as a setting based onthe Motion Picture of America Association film rating system.

FIG. 25 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 25 illustrates example embodiments where theoperation 240 may include at least one additional operation. Additionaloperations may include an operation 2502.

The operation 2502 illustrates providing a data display option ofdisplaying a data portion consistent with a workplace establishedsetting. Continuing the example above, data provider engine 108 (FIG.1A) may be in communication with Effect of content acceptabilitydetermination engine 106 (FIG. 1A), which may receive data and anassociated data content determination from data content determinationengine 104 (FIG. 1A) post obtaining of data by data obtainer engine 102(FIG. 1A). Effect of content acceptability determination engine 106 maytransfer effect of content acceptability determination to the dataprovider engine 108 to provide the data display option of displayingdata consistent with at least one workplace established setting. Forinstance, data content provider engine 108 may receive at least onedisplay instruction (e.g., do not display data) from at least onecomponent of Effect of content acceptability determination engine 106(FIG. 1A). At least one of virtual machines 11, 12, and/or 13 mayinclude one or more instruction generating modules configured to providean instruction to the Effect of content acceptability determinationengine 106 after a comparison of an activation of a link to a workplaceestablished setting stored in a copy of the user preference database 120(FIG. 1A) spawned on the virtual machine 11, 12, 13. Effect of contentacceptability determination engine 106 may communicate the displayinstruction to the data content provider engine 108. If displayed dataneeds to be modified to be consistent with at least one workplaceestablished setting, the data content provider engine 108 (FIG. 1A) maytransmit the modify data instruction to the data modification engine 122(FIG. 1A) for modification of the data. Data modification engine 122 maytransmit the modified data to the data content provider engine 108. Datacontent provider engine 108 may then display the data portion consistentwith the workplace established setting. For instance, the data displayoption may be displaying on a display of a real machine only a dataportion consistent with a workplace appropriateness desirability settingsuch as “display only non-obscene data.”

FIG. 26 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 26 illustrates example embodiments where theoperation 240 may include at least one additional operation. Additionaloperations may include an operation 2602, and/or an operation 2604.

The operation 2602 illustrates providing a data display option ofdisplaying a data portion consistent with a safety setting. Continuingthe example above, data provider engine 108 (FIG. 1A) may be incommunication with Effect of content acceptability determination engine106 (FIG. 1A), which may receive data and an associated data contentdetermination (e.g., data is an image) from data content determinationengine 104 (FIG. 1A) post obtaining of data by data obtainer engine 102(FIG. 1A). Effect of content acceptability determination engine 106 maytransfer effect of content acceptability determination to the dataprovider engine 108 to provide the data display option of displayingdata consistent with at least one safety setting. For instance, datacontent provider engine 108 may receive at least one display instruction(e.g., OK to display image) from at least one component of Effect ofcontent acceptability determination engine 106 (FIG. 1A). At least oneof virtual machines 11, 12, and/or 13 may include one or moreinstruction generating modules configured to provide an instruction tothe Effect of content acceptability determination engine 106 after acomparison of an activation of a link to a safety setting stored in acopy of the user preference database 120 (FIG. 1A) spawned on thevirtual machine 11, 12, 13. Effect of content acceptabilitydetermination engine 106 may communicate the display instruction to thedata content provider engine 108. If displayed data needs to be modifiedto be consistent with at least one safety setting, the data contentprovider engine 108 (FIG. 1A) may transmit the modify data instructionto the data modification engine 122 (FIG. 1A) for modification of thedata. Data modification engine 122 may transmit the modified data to thedata content provider engine 108. Data content provider engine 108 maythen display the data portion consistent with the safety setting. Forinstance, the data display option may be displaying on a display of areal machine only a data portion consistent with child safety settingsuch as “display only non-violent data,” or “display only ethnic andgender neutral data.”

Further, the operation 2604 illustrates providing a data display optionof displaying a data portion consistent with a public safety setting.Continuing the example above, data provider engine 108 (FIG. 1A) may bein communication with Effect of content acceptability determinationengine 106 (FIG. 1A), which may receive data and an associated datacontent determination (e.g., data is an image of format gif) from datacontent determination engine 104 (FIG. 1A) post obtaining of data bydata obtainer engine 102 (FIG. 1A). Effect of content acceptabilitydetermination engine 106 may transfer effect of content acceptabilitydetermination to the data provider engine 108 to provide the datadisplay option of displaying data consistent with at least onedesirability setting. For instance, data content provider engine 108 mayreceive at least one display instruction (e.g., OK to display image)from at least one component of Effect of content acceptabilitydetermination engine 106 (FIG. 1A). At least one of virtual machines 11,12, and/or 13 may include one or more instruction generating modulesconfigured to provide an instruction to the Effect of contentacceptability determination engine 106 after a comparison of anactivation of a link to a desirability setting stored in a copy of theuser preference database 120 (FIG. 1A) spawned on the virtual machine11, 12, 13. Effect of content acceptability determination engine 106 maycommunicate the display instruction to the data content provider engine108. If displayed data needs to be modified to be consistent with atleast one public safety setting, the data content provider engine 108(FIG. 1A) may transmit the modify data instruction to the datamodification engine 122 (FIG. 1A) for modification of the data. Datacontent provider engine 108 may then display the data portion consistentwith the public safety setting. For instance, the data display optionmay be displaying on a display of a real machine only a data portionconsistent with public safety setting such as “display onlynon-confidential data.”

FIG. 27 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 27 illustrates example embodiments where theoperation 240 may include at least one additional operation. Additionaloperations may include an operation 2702.

The operation 2702 illustrates providing a data display option ofdisplaying a data portion consistent with a child safety setting.Continuing the example above, data provider engine 108 (FIG. 1A) may bein communication with Effect of content acceptability determinationengine 106 (FIG. 1A), which may receive data and an associated datacontent determination from data content determination engine 104 (FIG.1A) post obtaining of data by data obtainer engine 102 (FIG. 1A). Effectof content acceptability determination engine 106 may transfer effect ofcontent acceptability determination to the data provider engine 108 toprovide the data display option of displaying data consistent with atleast one child safety setting. For instance, data content providerengine 108 may receive at least one display instruction from at leastone component of Effect of content acceptability determination engine106 (FIG. 1A). At least one of virtual machines 11, 12, and/or 13 mayinclude one or more instruction generating modules configured to providean instruction to the Effect of content acceptability determinationengine 106 after a comparison of an activation of a link to a childsafety setting stored in a copy of the user preference database 120(FIG. 1A) spawned on the virtual machine 11, 12, 13. Effect of contentacceptability determination engine 106 may communicate the displayinstruction to the data content provider engine 108. If displayed dataneeds to be modified to be consistent with at least one child safetysetting, the data content provider engine 108 (FIG. 1A) may transmit themodify data instruction to the data modification engine 122 (FIG. 1A)for modification of the data. Data content provider engine 108 may thendisplay the data portion consistent with the child safety setting. Forinstance, the data display option may be displaying on a display of areal machine only a data portion consistent with a child safety settingsuch as “display only non-violent data.”

FIG. 28 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 28 illustrates example embodiments where theoperation 240 may include at least one additional operation. Additionaloperations may include an operation 2802.

Further, the operation 2802 illustrates providing a data display optionof displaying a data portion consistent with a home safety setting.Continuing the example above, data provider engine 108 (FIG. 1A) may bein communication with Effect of content acceptability determinationengine 106 (FIG. 1A), which may receive data and an associated datacontent determination from data content determination engine 104 (FIG.1A) post obtaining of data by data obtainer engine 102 (FIG. 1A). Effectof content acceptability determination engine 106 may transfer effect ofcontent acceptability determination to the data provider engine 108 toprovide the data display option of displaying data consistent with atleast one home safety setting. For instance, data content providerengine 108 may receive at least one display instruction from at leastone component of Effect of content acceptability determination engine106 (FIG. 1A). At least one of virtual machines 11, 12, and/or 13 mayinclude one or more instruction generating modules configured to providean instruction to the Effect of content acceptability determinationengine 106 after a comparison of an activation of a link to a homesafety setting stored in a copy of the user preference database 120(FIG. 1A) spawned on the virtual machine 11, 12, 13. Effect of contentacceptability determination engine 106 may communicate the displayinstruction to the data content provider engine 108. If displayed dataneeds to be modified to be consistent with at least one home safetysetting, the data content provider engine 108 (FIG. 1A) may transmit themodify data instruction to the data modification engine 122 (FIG. 1A)for modification of the data. Data content provider engine 108 may thendisplay the data portion consistent with the home safety setting. Forinstance, the data display option may be displaying on a display of areal machine only a data portion consistent with home safety settingsuch as “okay to display private or confidential data.”

FIG. 29 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 29 illustrates example embodiments where theoperation 240 may include at least one additional operation. Additionaloperations may include an operation 3502.

The operation 2902 illustrates providing a data display option ofdisplaying a data portion consistent with a workplace safety setting.Continuing the example above, data provider engine 108 (FIG. 1A) may bein communication with Effect of content acceptability determinationengine 106 (FIG. 1A), which may receive data and an associated datacontent determination from data content determination engine 104 (FIG.1A) post obtaining of data by data obtainer engine 102 (FIG. 1A). Effectof content acceptability determination engine 106 may transfer effect ofcontent acceptability determination to the data provider engine 108 toprovide the data display option of displaying data consistent with atleast one workplace safety setting. For instance, data content providerengine 108 may receive at least one display instruction from at leastone component of Effect of content acceptability determination engine106 (FIG. 1A). Each of virtual machines 11, 12, and/or 13 may includeone or more instruction generating modules configured to provide aninstruction to the Effect of content acceptability determination engine106 after a comparison of an activation of a link to a workplace safetysetting stored in a copy of the user preference database 120 (FIG. 1A)spawned on the virtual machine 11, 12, 13. Effect of contentacceptability determination engine 106 may communicate the displayinstruction to the data content provider engine 108. If displayed dataneeds to be modified to be consistent with at least one workplace safetysetting, the data content provider engine 108 (FIG. 1A) may transmit themodify data instruction to the data modification engine 122 (FIG. 1A)for modification of the data. Data content provider engine 108 may thendisplay the data portion consistent with the workplace safety setting.For instance, the data display option may be displaying on a display ofa real machine only a data portion consistent with a workplace safetysetting such as “display only non-personal data.”

FIG. 30 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 30 illustrates example embodiments where theoperation 240 may include at least one additional operation. Additionaloperations may include an operation 3002, an operation 3004, and/or anoperation 3006.

The operation 3002 illustrates redirecting to alternative data.Continuing the example above, data provider engine 108 (FIG. 1A) may bein communication with Effect of content acceptability determinationengine 106 (FIG. 1A), which may receive data and an associated datacontent determination from data content determination engine 104 (FIG.1A) post obtaining of data by data obtainer engine 102 (FIG. 1A). Effectof content acceptability determination engine 106 may transfer effect ofcontent acceptability determination to the data provider engine 108, andan instruction to provide the data display option of redirecting toalternative data (e.g., another website). For instance, data contentprovider engine 108 may receive at least one redirect instruction fromat least one component of Effect of content acceptability determinationengine 106 (FIG. 1A). Each of virtual machines 11, 12, and/or 13 mayinclude one or more instruction generating modules configured to providea redirect instruction to the Effect of content acceptabilitydetermination engine 106 after a comparison of an activation of a linkto a user preference stored in a copy of the user preference database120 (FIG. 1A) spawned on the virtual machine 11, 12, 13. Effect ofcontent acceptability determination engine 106 may communicate theredirect instruction to the data content provider engine 108. The datacontent provider engine 108 may transmit the redirect data instructionto the data redirection engine 128 for redirection to alternative data.The data redirection engine 128 may transmit the redirection to the datacontent provider engine 108. Data content provider engine 108 may thendisplay the alternative data.

Further, the operation 3004 illustrates automatically redirecting toalternative data. Continuing the example above, data provider engine 108(FIG. 1A) may be in communication with Effect of content acceptabilitydetermination engine 106 (FIG. 1A), which may receive data and anassociated data content determination from data content determinationengine 104 (FIG. 1A) post obtaining of data by data obtainer engine 102(FIG. 1A). Effect of content acceptability determination engine 106 maytransfer effect of content acceptability determination to the dataprovider engine 108 to provide the data display option of redirecting toalternative data (e.g., another website) consistent with a userpreference. For instance, data content provider engine 108 may receiveat least one redirect instruction from at least one component of Effectof content acceptability determination engine 106 (FIG. 1A). At leastone of virtual machines 11, 12, and/or 13 may include one or moreinstruction generating modules configured to provide a redirectinstruction to the Effect of content acceptability determination engine106 after a comparison of an activation of a link to a user preferencestored in a copy of the user preference database 120 (FIG. 1A) spawnedon the virtual machine 11, 12, 13. Effect of content acceptabilitydetermination engine 106 may communicate a redirect to alternative dataconsistent with the user preference instruction to the data contentprovider engine 108. The data content provider engine 108 may transmitthe redirect data instruction to the data redirection engine 128 forredirection to alternative data consistent with the user preference. Thedata redirection engine 128 may transmit the redirection to the datacontent provider engine 108. Data content provider engine 108 may thenautomatically (e.g., prior to alerting a user) display the alternativedata. For instance, a real machine 130 may be automatically redirectedto an acceptable web link, or a page of acceptable data.

Further, the operation 3006 illustrates providing a list of selectablealternative data options. Continuing the example above, data providerengine 108 (FIG. 1A) may be in communication with Effect of contentacceptability determination engine 106 (FIG. 1A), which may receive dataand an associated data content determination from data contentdetermination engine 104 (FIG. 1A) post obtaining of data by dataobtainer engine 102 (FIG. 1A). Effect of content acceptabilitydetermination engine 106 may transfer effect of content acceptabilitydetermination to the data provider engine 108, and an instruction toprovide the data display option of providing a list of selectablealternative data options (e.g., a list of alternative websites)consistent with a user preference. For instance, data content providerengine 108 may receive at least one provide selectable alternativesinstruction from at least one component of Effect of contentacceptability determination engine 106 (FIG. 1A). At least one ofvirtual machines 11, 12, and/or 13 may include one or more instructiongenerating modules configured to transmit a provide selectablealternatives instruction to the Effect of content acceptabilitydetermination engine 106 after a comparison of an activation of a linkto a user preference stored in a copy of the user preference database120 (FIG. 1A) spawned on the virtual machine 11, 12, 13. Effect ofcontent acceptability determination engine 106 may communicate theprovide selectable alternatives instruction to the data content providerengine 108. The data content provider engine 108 may transmit theprovide selectable alternatives instruction to the data redirectionengine 128 to provide selectable alternatives consistent with the userpreference. The data redirection engine 128 may transmit the list ofselectable alternatives to the data content provider engine 108. Datacontent provider engine 108 may then display the list of selectablealternatives. For instance, the list of selectable alternative dataoptions may include a list of acceptable web links or a selectable listof web pages. Selectable web links and web pages may include a thumbnailimage of the first page of the web link or of the web page.

FIG. 31 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 31 illustrates example embodiments where theoperation 240 may include at least one additional operation. Additionaloperations may include an operation 3102, and/or an operation 3104.

The operation 3102 illustrates displaying alternative data consistentwith a privacy related setting. Continuing the example above, dataprovider engine 108 (FIG. 1A) may be in communication with Effect ofcontent acceptability determination engine 106 (FIG. 1A), which mayreceive data and an associated data content determination (e.g., datadoes not contain spyware) from data content determination engine 104(FIG. 1A) post obtaining of data by data obtainer engine 102 (FIG. 1A).Effect of content acceptability determination engine 106 may transfereffect of content acceptability determination to the data providerengine 108, and an instruction to provide the data display option ofdisplaying data consistent with at least one privacy related setting.For instance, data content provider engine 108 may receive at least onedisplay instruction (e.g., OK to display webpage) from at least onecomponent of Effect of content acceptability determination engine 106(FIG. 1A). At least one of virtual machines 11, 12, and/or 13 mayinclude one or more instruction generating modules configured to providean instruction to the Effect of content acceptability determinationengine 106 after a comparison of an activation of a link to a privacyrelated setting stored in a copy of the user preference database 120(FIG. 1A) spawned on the virtual machine 11, 12, 13. Effect of contentacceptability determination engine 106 may communicate the displayinstruction to the data content provider engine 108. If data needs to bemodified to be consistent with at least one privacy related setting, thedata content provider engine 108 may transmit the modify datainstruction to the data modification engine 122 for modification of thedata. Data modification engine 122 may transmit the modified data to thedata content provider engine 108. Data content provider engine 108 maythen display the data consistent with the privacy related setting. Forinstance, a portion of a returned webpage including data requestingprivate user information such as a user's social security number ore-mail address may be removed from the web page prior to display of thedata on a computer screen. Further specific examples include a webpageor website data may be determined to be displayable if the datasatisfies a setting such as a privacy related setting such as a settingrelating to a user's biographical information or financial information,a webpage or website data may be determined to be displayable if thedata satisfies a group privacy related setting such as a work group(e.g., employees of a company), a peer group (e.g., members of a bookclub), or a family group (e.g., members of family unit) privacy relatedsetting, or a webpage or website data may be determined to bedisplayable if the data satisfies a privacy setting determined by acorporation or other organization to maintain corporate or organizationprivacy.

Further, the operation 3104 illustrates displaying alternative dataconsistent with a customized user setting. Continuing the example above,data provider engine 108 (FIG. 1A) may be in communication with Effectof content acceptability determination engine 106 (FIG. 1A), which mayreceive data and an associated data content determination (e.g., datadoes not contain malware) from data content determination engine 104(FIG. 1A) post obtaining of data by data obtainer engine 102 (FIG. 1A).Effect of content acceptability determination engine 106 may transfereffect of content acceptability determination to the data providerengine 108, and an instruction to provide the data display option ofdisplaying data consistent with at least one user setting. For instance,data content provider engine 108 may receive at least one displayinstruction (e.g., OK to display webpage) from at least one component ofEffect of content acceptability determination engine 106 (FIG. 1A). Atleast one of virtual machines 11, 12, and/or 13 may include one or moreinstruction generating modules configured to provide an instruction tothe Effect of content acceptability determination engine 106 after acomparison of an activation of a link to a user setting stored in a copyof the user preference database 120 (FIG. 1A) spawned on the virtualmachine 11, 12, 13. Effect of content acceptability determination engine106 may communicate the display instruction to the data content providerengine 108. If data needs to be modified to be consistent with at leastone user setting, the data content provider engine 108 may transmit themodify data instruction to the data modification engine 122 formodification of the data. Data modification engine 122 may transmit themodified data to the data content provider engine 108. Data contentprovider engine 108 may then display the data consistent with the usersetting. Thus, a webpage or website data may be determined to bedisplayable if the data satisfies a user setting when at least one ofvirtual machines 11, 12, and/or 13 compares the data to the usersetting. For instance, a portion of a webpage produced by a searchincluding non-English text may be removed from the web page prior todisplay of the data on a computer screen. Further, in one specificexample, a webpage or website data may be determined to be displayableif the data satisfies a peer user setting, or a webpage or website datamay be determined to be displayable if the data satisfies, for instance,a corporate user setting.

FIG. 32 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 32 illustrates example embodiments where theoperation 240 may include at least one additional operation. Additionaloperations may include an operation 3202, and/or an operation 3204.

The operation 3202 illustrates displaying alternative data consistentwith a desirability setting. Continuing the example above, data providerengine 108 (FIG. 1A) may be in communication with Effect of contentacceptability determination engine 106 (FIG. 1A), which may receive dataand an associated data content determination (e.g., data is an image)from data content determination engine 104 (FIG. 1A) post obtaining ofdata by data obtainer engine 102 (FIG. 1A). Effect of contentacceptability determination engine 106 may transfer effect of contentacceptability determination to the data provider engine 108, and aninstruction to provide the data display option of displaying dataconsistent with at least one desirability setting. For instance, datacontent provider engine 108 may receive at least one display instruction(e.g., OK to display image) from at least one component of Effect ofcontent acceptability determination engine 106 (FIG. 1A). At least oneof virtual machines 11, 12, and/or 13 may include one or moreinstruction generating modules configured to provide an instruction tothe Effect of content acceptability determination engine 106 after acomparison of an activation of a link to a desirability setting storedin a copy of the user preference database 120 (FIG. 1A) spawned on thevirtual machine 11, 12, 13. Effect of content acceptabilitydetermination engine 106 may communicate the display instruction to thedata content provider engine 108. If data needs to be modified to beconsistent with at least one desirability setting, the data contentprovider engine 108 may transmit the modify data instruction to the datamodification engine 122 for modification of the data. Data modificationengine 122 may transmit the modified data to the data content providerengine 108. Data content provider engine 108 may then display the dataportion consistent with the desirability setting. For instance, the datadisplay option may be displaying on a display of a real machine only adata portion consistent with a Christian desirability setting such as“display only Christianity related data.” In other examples, a webpageor website data may be determined to be displayable if the datasatisfies a desirability setting, a webpage or website data may bedetermined to be displayable if the data satisfies a religiousdesirability setting such as a Christian, Jewish, and/or Muslim, basedreligious desirability setting, or may be based on any other major,minor or alternative religious desirability setting, a webpage orwebsite data may be determined to be displayable if the data satisfies apolitical desirability setting such as a Republican, Democratic,Libertarian or Green Party political desirability setting, a webpage orwebsite data may be determined to be displayable if the data satisfies acultural desirability setting such as a religious, ethnic, regional, orheritage based cultural desirability setting or any other culturaldesirability setting, a webpage or website data may be determined to bedisplayable if the data satisfies a theme related desirability settingsuch as boating or card games, or a webpage or website data may bedetermined to be displayable if the data satisfies an ageappropriateness desirability setting such as a setting based on theMotion Picture of America Association film rating system.

Further, the operation 3204 illustrates displaying alternative dataconsistent with a workplace established setting. Continuing the exampleabove, data provider engine 108 (FIG. 1A) may be in communication withEffect of content acceptability determination engine 106 (FIG. 1A),which may receive data and an associated data content determination(e.g., data is a social networking site) from data content determinationengine 104 (FIG. 1A) post obtaining of data by data obtainer engine 102(FIG. 1A). Effect of content acceptability determination engine 106 maytransfer effect of content acceptability determination to the dataprovider engine 108, and an instruction to provide the data displayoption of displaying data consistent with at least one workplaceestablished setting. For instance, data content provider engine 108 mayreceive at least one display instruction (e.g., do not display data)from at least one component of Effect of content acceptabilitydetermination engine 106 (FIG. 1A). At least one of virtual machines 11,12, and/or 13 may include one or more instruction generating modulesconfigured to provide an instruction to the Effect of contentacceptability determination engine 106 after a comparison of anactivation of a link to a workplace established setting stored in a copyof the user preference database 120 (FIG. 1A) spawned on the virtualmachine 11, 12, 13. Effect of content acceptability determination engine106 may communicate the display instruction to the data content providerengine 108. If data needs to be modified to be consistent with at leastone workplace established setting, the data content provider engine 108may transmit the modify data instruction to the data modification engine122 for modification of the data. Data modification engine 122 maytransmit the modified data to the data content provider engine 108. Datacontent provider engine 108 may then display the data portion consistentwith the workplace established setting. For instance, the data displayoption may be displaying on a display of a real machine only a dataportion consistent with a workplace appropriateness desirability settingsuch as “display only non-obscene data.”

FIG. 33 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 33 illustrates example embodiments where theoperation 240 may include at least one additional operation. Additionaloperations may include an operation 3302.

The operation 3302 illustrates displaying alternative data consistentwith a user history setting. Continuing the example above, data providerengine 108 (FIG. 1A) may be in communication with Effect of contentacceptability determination engine 106 (FIG. 1A), which may receive dataand an associated data content determination from data contentdetermination engine 104 (FIG. 1A) post obtaining of data by dataobtainer engine 102 (FIG. 1A). Effect of content acceptabilitydetermination engine 106 may transfer effect of content acceptabilitydetermination to the data provider engine 108, and an instruction toprovide the data display option of redirecting to alternative dataconsistent with a user history setting (e.g., another website). Forinstance, data content provider engine 108 may receive at least oneredirect instruction from at least one component of Effect of contentacceptability determination engine 106 (FIG. 1A). At least one ofvirtual machines 11, 12, and/or 13 may include one or more instructiongenerating modules configured to provide a redirect instruction to theEffect of content acceptability determination engine 106 after acomparison of an activation of a link to a user history setting storedin a copy of the user preference database 120 (FIG. 1A) spawned on thevirtual machine 11, 12, 13. Effect of content acceptabilitydetermination engine 106 may communicate the redirect to alternativedata consistent with a user history setting instruction to the datacontent provider engine 108. The data content provider engine 108 maytransmit the redirect data instruction to the data redirection engine128 for redirection to alternative data consistent with a user historysetting. The data redirection engine 128 may transmit the redirection tothe data content provider engine 108. Data content provider engine 108may then display the alternative data. For instance, displayedalternative data may be consistent with a user history such as havingviewed only music related data and pages.

FIG. 34 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 34 illustrates example embodiments where theoperation 240 may include at least one additional operation. Additionaloperations may include an operation 3402, an operation 3404, and/or anoperation 3406.

The operation 3402 illustrates displaying alternative data consistentwith a safety setting. Continuing the example above, data providerengine 108 (FIG. 1A) may be in communication with Effect of contentacceptability determination engine 106 (FIG. 1A), which may receive dataand an associated data content determination from data contentdetermination engine 104 (FIG. 1A) post obtaining of data by dataobtainer engine 102 (FIG. 1A). Effect of content acceptabilitydetermination engine 106 may transfer effect of content acceptabilitydetermination to the data provider engine 108, and an instruction toprovide the data display option of redirecting to alternative dataconsistent with a safety setting (e.g., another website). For instance,data content provider engine 108 may receive at least one redirectinstruction from at least one component of Effect of contentacceptability determination engine 106 (FIG. 1A). At least one ofvirtual machines 11, 12, and/or 13 may include one or more instructiongenerating modules configured to provide a redirect instruction to theEffect of content acceptability determination engine 106 after acomparison of an activation of a link to a safety setting stored in acopy of the user preference database 120 (FIG. 1A) spawned on thevirtual machine 11, 12,13. Effect of content acceptability determinationengine 106 may communicate the redirect to alternative data consistentwith a safety setting instruction to the data content provider engine108. The data content provider engine 108 may transmit the redirect datainstruction to the data redirection engine 128 for redirection toalternative data consistent with a safety setting. The data redirectionengine 128 may transmit the redirection to the data content providerengine 108. Data content provider engine 108 may then display thealternative data. Displaying alternative data consistent with a safetysetting may include displaying a different webpage including onlyinformation consistent with a safety setting such as “do not displaylinks requesting credit card information.”

Further, the operation 3404 illustrates displaying alternative dataconsistent with a public safety setting. Continuing the example above,data provider engine 108 (FIG. 1A) may be in communication with Effectof content acceptability determination engine 106 (FIG. 1A), which mayreceive data and an associated data content determination from datacontent determination engine 104 (FIG. 1A) post obtaining of data bydata obtainer engine 102 (FIG. 1A). Effect of content acceptabilitydetermination engine 106 may transfer effect of content acceptabilitydetermination to the data provider engine 108, and an instruction toprovide the data display option of redirecting to alternative dataconsistent with a public safety setting (e.g., another website). Forinstance, data content provider engine 108 may receive at least oneredirect instruction from at least one component of Effect of contentacceptability determination engine 106 (FIG. 1A). At least one ofvirtual machines 11, 12, and/or 13 may include one or more instructiongenerating modules configured to provide a redirect instruction to theEffect of content acceptability determination engine 106 after acomparison of an activation of a link to a public safety setting storedin a copy of the user preference database 120 (FIG. 1A) spawned on thevirtual machine 11, 12, 13. Effect of content acceptabilitydetermination engine 106 may communicate the redirect to alternativedata consistent with a public safety setting instruction to the datacontent provider engine 108. The data content provider engine 108 maytransmit the redirect data instruction to the data redirection engine128 for redirection to alternative data consistent with a public safetysetting. The data redirection engine 128 may transmit the redirection tothe data content provider engine 108. Data content provider engine 108may then display the alternative data. Displaying alternative dataconsistent with a public safety setting may include displaying adifferent webpage including only information consistent with a publicsafety setting such as “display only non-confidential data.” Publicsafety setting may include a transmittable information safety setting, aviewable information safety setting and a receivable information safetysetting. Transmittable or viewable information may be private userinformation, such as credit card numbers, bank accounts, personalidentification information or any other personal user information.Receivable information may be any information such as text, images, avirus, spyware, or any other information that a user's real machine maybe capable of receiving from an external source.

The operation 3406 illustrates displaying alternative data consistentwith a home safety setting. Continuing the example above, data providerengine 108 (FIG. 1A) may be in communication with Effect of contentacceptability determination engine 106 (FIG. 1A), which may receive dataand an associated data content determination from data contentdetermination engine 104 (FIG. 1A) post obtaining of data by dataobtainer engine 102 (FIG. 1A). Effect of content acceptabilitydetermination engine 106 may transfer effect of content acceptabilitydetermination to the data provider engine 108, and an instruction toprovide the data display option of redirecting to alternative dataconsistent with a home safety setting (e.g., another website). Forinstance, data content provider engine 108 may receive at least oneredirect instruction from at least one component of Effect of contentacceptability determination engine 106 (FIG. 1A). At least one ofvirtual machines 11, 12, and/or 13 may include one or more instructiongenerating modules configured to provide a redirect instruction to theEffect of content acceptability determination engine 106 after acomparison of an activation of a link to a home safety setting stored ina copy of the user preference database 120 (FIG. 1A) spawned on thevirtual machine 11, 12, 13. Effect of content acceptabilitydetermination engine 106 may communicate the redirect to alternativedata consistent with a home safety setting instruction to the datacontent provider engine 108. The data content provider engine 108 maytransmit the redirect data instruction to the data redirection engine128 for redirection to alternative data consistent with a home safetysetting. The data redirection engine 128 may transmit the redirection tothe data content provider engine 108. Data content provider engine 108may then display the alternative data. Displaying alternative dataconsistent with a home safety setting may include displaying a differentwebpage including only information consistent with a home safety settingsuch as “do not display links requesting address information.”

FIG. 35 illustrates alternative embodiments of the example operationalflow 200 of FIG. 2. FIG. 35 illustrates example embodiments where theoperation 240 may include at least one additional operation. Additionaloperations may include an operation 3502, and/or an operation 3504.

The operation 3502 illustrates displaying alternative data consistentwith a workplace safety setting. Continuing the example above, dataprovider engine 108 (FIG. 1A) may be in communication with Effect ofcontent acceptability determination engine 106 (FIG. 1A), which mayreceive data and an associated data content determination from datacontent determination engine 104 (FIG. 1A) post obtaining of data bydata obtainer engine 102 (FIG. 1A). Effect of content acceptabilitydetermination engine 106 may transfer effect of content acceptabilitydetermination to the data provider engine 108, and an instruction toprovide the data display option of redirecting to alternative dataconsistent with a workplace safety setting (e.g., another website). Forinstance, data content provider engine 108 may receive at least oneredirect instruction from at least one component of Effect of contentacceptability determination engine 106 (FIG. 1A). At least one ofvirtual machines 11, 12, and/or 13 may include one or more instructiongenerating modules configured to provide a redirect instruction to theEffect of content acceptability determination engine 106 after acomparison of an activation of a link to a workplace safety settingstored in a copy of the user preference database 120 (FIG. 1A) spawnedon the virtual machine 11, 12, 13. Effect of content acceptabilitydetermination engine 106 may communicate the redirect to alternativedata consistent with a workplace safety setting instruction to the datacontent provider engine 108. The data content provider engine 108 maytransmit the redirect data instruction to the data redirection engine128 for redirection to alternative data consistent with a workplacesafety setting. The data redirection engine 128 may transmit theredirection to the data content provider engine 108. Data contentprovider engine 108 may then display the alternative data. Displayingalternative data consistent with a workplace safety setting may includedisplaying a different webpage including only information consistentwith a workplace safety setting such as “do not display links requestinginformation on this computer.”

Further, the operation 3504 illustrates displaying alternative dataconsistent with a child safety setting. Continuing the example above,data provider engine 108 (FIG. 1A) may be in communication with Effectof content acceptability determination engine 106 (FIG. 1A), which mayreceive data and an associated data content determination from datacontent determination engine 104 (FIG. 1A) post obtaining of data bydata obtainer engine 102 (FIG. 1A). Effect of content acceptabilitydetermination engine 106 may transfer effect of content acceptabilitydetermination to the data provider engine 108 to provide the datadisplay option of redirecting to alternative data consistent with achild safety setting (e.g., another website). For instance, data contentprovider engine 108 may receive at least one redirect instruction fromat least one component of Effect of content acceptability determinationengine 106 (FIG. 1A). At least one of virtual machines 11, 12, and/or 13may include one or more instruction generating modules configured toprovide a redirect instruction to the Effect of content acceptabilitydetermination engine 106 after a comparison of an activation of a linkto a child safety setting stored in a copy of the user preferencedatabase 120 (FIG. 1A) spawned on the virtual machine 11, 12, 13. Effectof content acceptability determination engine 106 may communicate theredirect to alternative data consistent with a child safety settinginstruction to the data content provider engine 108. The data contentprovider engine 108 may transmit the redirect data instruction to thedata redirection engine 128 for redirection to alternative dataconsistent with a child safety setting. The data redirection engine 128may transmit the redirection to the data content provider engine 108.Data content provider engine 108 may then display the alternative data.Displaying alternative data consistent with a child safety setting mayinclude displaying a different webpage including only informationconsistent with a child safety setting such as “do not display linkscontaining trailers for rated ‘R’ movies.”

Following are a series of flowcharts depicting implementations. For easeof understanding, the flowcharts are organized such that the initialflowcharts present implementations via an example implementation andthereafter the following flowcharts present alternate implementationsand/or expansions of the initial flowchart(s) as either sub-componentoperations or additional component operations building on one or moreearlier-presented flowcharts. Those having skill in the art willappreciate that the style of presentation utilized herein (e.g.,beginning with a presentation of a flowchart(s) presenting an exampleimplementation and thereafter providing additions to and/or furtherdetails in subsequent flowcharts) generally allows for a rapid and easyunderstanding of the various process implementations. In addition, thoseskilled in the art will further appreciate that the style ofpresentation used herein also lends itself well to modular and/orobject-oriented program design paradigms.

Those having skill in the art will recognize that the state of the arthas progressed to the point where there is little distinction leftbetween hardware, software, and/or firmware implementations of aspectsof systems; the use of hardware, software, and/or firmware is generally(but not always, in that in certain contexts the choice between hardwareand software can become significant) a design choice representing costvs. efficiency tradeoffs. Those having skill in the art will appreciatethat there are various vehicles by which processes and/or systems and/orother technologies described herein can be effected (e.g., hardware,software, and/or firmware), and that the preferred vehicle will varywith the context in which the processes and/or systems and/or othertechnologies are deployed. For example, if an implementer determinesthat speed and accuracy are paramount, the implementer may opt for amainly hardware and/or firmware vehicle; alternatively, if flexibilityis paramount, the implementer may opt for a mainly softwareimplementation; or, yet again alternatively, the implementer may opt forsome combination of hardware, software, and/or firmware. Hence, thereare several possible vehicles by which the processes and/or devicesand/or other technologies described herein may be effected, none ofwhich is inherently superior to the other in that any vehicle to beutilized is a choice dependent upon the context in which the vehiclewill be deployed and the specific concerns (e.g., speed, flexibility, orpredictability) of the implementer, any of which may vary. Those skilledin the art will recognize that optical aspects of implementations willtypically employ optically-oriented hardware, software, and or firmware.

In some implementations described herein, logic and similarimplementations may include software or other control structuressuitable to operation. Electronic circuitry, for example, may manifestone or more paths of electrical current constructed and arranged toimplement various logic functions as described herein. In someimplementations, one or more media are configured to bear adevice-detectable implementation if such media hold or transmit aspecial-purpose device instruction set operable to perform as describedherein. In some variants, for example, this may manifest as an update orother modification of existing software or firmware, or of gate arraysor other programmable hardware, such as by performing a reception of ora transmission of one or more instructions in relation to one or moreoperations described herein. Alternatively or additionally, in somevariants, an implementation may include special-purpose hardware,software, firmware components, and/or general-purpose componentsexecuting or otherwise invoking special-purpose components.Specifications or other implementations may be transmitted by one ormore instances of tangible transmission media as described herein,optionally by packet transmission or otherwise by passing throughdistributed media at various times.

Alternatively or additionally, implementations may include executing aspecial-purpose instruction sequence or otherwise invoking circuitry forenabling, triggering, coordinating, requesting, or otherwise causing oneor more occurrences of any functional operations described above. Insome variants, operational or other logical descriptions herein may beexpressed directly as source code and compiled or otherwise invoked asan executable instruction sequence. In some contexts, for example, C++or other code sequences can be compiled directly or otherwiseimplemented in high-level descriptor languages (e.g., alogic-synthesizable language, a hardware description language, ahardware design simulation, and/or other such similar mode(s) ofexpression). Alternatively or additionally, some or all of the logicalexpression may be manifested as a Verilog-type hardware description orother circuitry model before physical implementation in hardware,especially for basic operations or timing-critical applications. Thoseskilled in the art will recognize how to obtain, configure, and optimizesuitable transmission or computational elements, material supplies,actuators, or other common structures in light of these teachings.

The foregoing detailed description has set forth various embodiments ofthe devices and/or processes via the use of block diagrams, flowcharts,and/or examples. Insofar as such block diagrams, flowcharts, and/orexamples contain one or more functions and/or operations, it will beunderstood by those within the art that each function and/or operationwithin such block diagrams, flowcharts, or examples can be implemented,individually and/or collectively, by a wide range of hardware, software,firmware, or virtually any combination thereof. In one embodiment,several portions of the subject matter described herein may beimplemented via Application Specific Integrated Circuits (ASICs), FieldProgrammable Gate Arrays (FPGAs), digital signal processors (DSPs), orother integrated formats. However, those skilled in the art willrecognize that some aspects of the embodiments disclosed herein, inwhole or in part, can be equivalently implemented in integratedcircuits, as one or more computer programs running on one or morecomputers (e.g., as one or more programs running on one or more computersystems), as one or more programs running on one or more processors(e.g., as one or more programs running on one or more microprocessors),as firmware, or as virtually any combination thereof, and that designingthe circuitry and/or writing the code for the software and or firmwarewould be well within the skill of one of skill in the art in light ofthis disclosure. In addition, those skilled in the art will appreciatethat the mechanisms of the subject matter described herein are capableof being distributed as a program product in a variety of forms, andthat an illustrative embodiment of the subject matter described hereinapplies regardless of the particular type of signal bearing medium usedto actually carry out the distribution. Examples of a signal bearingmedium include, but are not limited to, the following: a recordable typemedium such as a floppy disk, a hard disk drive, a Compact Disc (CD), aDigital Video Disk (DVD), a digital tape, a computer memory, etc.; and atransmission type medium such as a digital and/or an analogcommunication medium (e.g., a fiber optic cable, a waveguide, a wiredcommunications link, a wireless communication link (e.g., transmitter,receiver, transmission logic, reception logic, etc.), etc.).

In a general sense, those skilled in the art will recognize that thevarious aspects described herein which can be implemented, individuallyand/or collectively, by a wide range of hardware, software, firmware,and/or any combination thereof can be viewed as being composed ofvarious types of “electrical circuitry.” Consequently, as used herein“electrical circuitry” includes, but is not limited to, electricalcircuitry having at least one discrete electrical circuit, electricalcircuitry having at least one integrated circuit, electrical circuitryhaving at least one application specific integrated circuit, electricalcircuitry forming a general purpose computing device configured by acomputer program (e.g., a general purpose computer configured by acomputer program which at least partially carries out processes and/ordevices described herein, or a microprocessor configured by a computerprogram which at least partially carries out processes and/or devicesdescribed herein), electrical circuitry forming a memory device (e.g.,forms of memory (e.g., random access, flash, read only, etc.)), and/orelectrical circuitry forming a communications device (e.g., a modem,communications switch, optical-electrical equipment, etc.). Those havingskill in the art will recognize that the subject matter described hereinmay be implemented in an analog or digital fashion or some combinationthereof.

Those skilled in the art will recognize that at least a portion of thedevices and/or processes described herein can be integrated into a dataprocessing system. Those having skill in the art will recognize that adata processing system generally includes one or more of a system unithousing, a video display device, memory such as volatile or non-volatilememory, processors such as microprocessors or digital signal processors,computational entities such as operating systems, drivers, graphicaluser interfaces, and applications programs, one or more interactiondevices (e.g., a touch pad, a touch screen, an antenna, etc.), and/orcontrol systems including feedback loops and control motors (e.g.,feedback for sensing position and/or velocity; control motors for movingand/or adjusting components and/or quantities). A data processing systemmay be implemented utilizing suitable commercially available components,such as those typically found in data computing/communication and/ornetwork computing/communication systems.

The herein described subject matter sometimes illustrates differentcomponents contained within, or connected with, different othercomponents. It is to be understood that such depicted architectures aremerely exemplary, and that in fact many other architectures may beimplemented which achieve the same functionality. In a conceptual sense,any arrangement of components to achieve the same functionality iseffectively “associated” such that the desired functionality isachieved. Hence, any two components herein combined to achieve aparticular functionality can be seen as “associated with” each othersuch that the desired functionality is achieved, irrespective ofarchitectures or intermedial components. Likewise, any two components soassociated can also be viewed as being “operably connected”, or“operably coupled”, to each other to achieve the desired functionality,and any two components capable of being so associated can also be viewedas being “operably couplable”, to each other to achieve the desiredfunctionality. Specific examples of operably couplable include but arenot limited to physically mateable and/or physically interactingcomponents, and/or wirelessly interactable, and/or wirelesslyinteracting components, and/or logically interacting, and/or logicallyinteractable components.

In some instances, one or more components may be referred to herein as“configured to,” “configurable to,” “operable/operative to,”“adapted/adaptable,” “able to,” “conformable/conformed to,” etc. Thoseskilled in the art will recognize that “configured to” can generallyencompass active-state components and/or inactive-state componentsand/or standby-state components, unless context requires otherwise.

While particular aspects of the present subject matter described hereinhave been shown and described, it will be apparent to those skilled inthe art that, based upon the teachings herein, changes and modificationsmay be made without departing from the subject matter described hereinand its broader aspects and, therefore, the appended claims are toencompass within their scope all such changes and modifications as arewithin the true spirit and scope of the subject matter described herein.It will be understood by those within the art that, in general, termsused herein, and especially in the appended claims (e.g., bodies of theappended claims) are generally intended as “open” terms (e.g., the term“including” should be interpreted as “including but not limited to,” theterm “having” should be interpreted as “having at least,” the term“includes” should be interpreted as “includes but is not limited to,”etc.). It will be further understood by those within the art that if aspecific number of an introduced claim recitation is intended, such anintent will be explicitly recited in the claim, and in the absence ofsuch recitation no such intent is present. For example, as an aid tounderstanding, the following appended claims may contain usage of theintroductory phrases “at least one” and “one or more” to introduce claimrecitations. However, the use of such phrases should not be construed toimply that the introduction of a claim recitation by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim recitation to claims containing only one suchrecitation, even when the same claim includes the introductory phrases“one or more” or “at least one” and indefinite articles such as “a” or“an” (e.g., “a” and/or “an” should typically be interpreted to mean “atleast one” or “one or more”); the same holds true for the use ofdefinite articles used to introduce claim recitations. In addition, evenif a specific number of an introduced claim recitation is explicitlyrecited, those skilled in the art will recognize that such recitationshould typically be interpreted to mean at least the recited number(e.g., the bare recitation of “two recitations,” without othermodifiers, typically means at least two recitations, or two or morerecitations). Furthermore, in those instances where a conventionanalogous to “at least one of A, B, and C, etc.” is used, in generalsuch a construction is intended in the sense one having skill in the artwould understand the convention (e.g., “a system having at least one ofA, B, and C” would include but not be limited to systems that have Aalone, B alone, C alone, A and B together, A and C together, B and Ctogether, and/or A, B, and C together, etc.). In those instances where aconvention analogous to “at least one of A, B, or C, etc.” is used, ingeneral such a construction is intended in the sense one having skill inthe art would understand the convention (e.g., “a system having at leastone of A, B, or C” would include but not be limited to systems that haveA alone, B alone, C alone, A and B together, A and C together, B and Ctogether, and/or A, B, and C together, etc.). It will be furtherunderstood by those within the art that typically a disjunctive wordand/or phrase presenting two or more alternative terms, whether in thedescription, claims, or drawings, should be understood to contemplatethe possibilities of including one of the terms, either of the terms, orboth terms. For example, the phrase “A or B” will be typicallyunderstood to include the possibilities of “A” or “B” or “A and B.”

With respect to the appended claims, those skilled in the art willappreciate that recited operations therein may generally be performed inany order. Also, although various operational flows are presented in asequence(s), it should be understood that the various operations may beperformed in other orders than those which are illustrated, or may beperformed concurrently. Examples of such alternate orderings may includeoverlapping, interleaved, interrupted, reordered, incremental,preparatory, supplemental, simultaneous, reverse, or other variantorderings, unless context dictates otherwise. Furthermore, terms like“responsive to,” “related to,” or other past-tense adjectives aregenerally not intended to exclude such variants, unless context dictatesotherwise.

1. A computationally-implemented method comprising: obtaining at least aportion of data from a data source; determining a content of the data;determining an acceptability of an effect of content of the data atleast in part via at least two virtual machine representations of atleast a part of a real machine having at least one end-user specifiedpreference, at least one of the at least two virtual machinerepresentations operating at least in part on an individual core of amulti-core system; and displaying at least one data display option basedon the determining an acceptability of a content of the data.
 2. Thecomputationally-implemented method of claim 1, wherein the determining acontent of the data comprises: examining a database of known content fordata content information.
 3. The computationally-implemented method ofclaim 1, wherein the determining a content of the data comprises:traversing at least a portion of the data in real time.
 4. Thecomputationally-implemented method of claim 1, wherein the determining acontent of the data comprises: locally examining at least a portion ofthe data.
 5. The computationally-implemented method of claim 1, whereinthe determining an acceptability of an effect of content of the data atleast in part via at least two virtual machine representations of atleast a part of a real machine having at least one end-user specifiedpreference, at least one of the at least two viral machinerepresentations operating at least in part on an individual core of amulti-core system comprises: examining at least a portion of the data tolocate references to additional content.
 6. Thecomputationally-implemented method of claim 5, wherein the examining atleast a portion of the data to locate references to additional contentcomprises: determining whether the data references additional datacontent information when loading.
 7. The computationally-implementedmethod of claim 5, wherein the examining at least a portion of the datato locate references to additional content comprises: issuing a requestto a remote computer for additional data content information.
 8. Thecomputationally-implemented method of claim 1, wherein the determiningan acceptability of an effect of content of the data at least in partvia at least two virtual machine representations of at least a part of areal machine having at least one end-user specified preference, at leastone of the at least two virtual machine representations operating atleast in part on an individual core of a multi-core system comprises:determining an acceptability of an effect of content of the data atleast in part via at least two virtual machine representations of atleast a part of a real machine having at least one end-user specifiedpreference, at least one of the at least two virtual machinerepresentations operating at least in part on an individual core of amulti-core system at least partially resident within a real machine. 9.The computationally-implemented method of claim 1, wherein thedetermining an acceptability of an effect of content of the data atleast in part via at least two virtual machine representations of atleast a part of a real machine having at least one end-user specifiedpreference, at least one of the at least two virtual machinerepresentations operating at least in part on an individual core of amulti-core system comprises: determining an acceptability of an effectof content of the data at least in part via at least two virtual machinerepresentations of at least a part of a real machine having at least oneend-user specified preference, at least one of the at least two virtualmachine representations operating at least in part on an individual coreof a multi-core system at least partially non-resident within a realmachine.
 10. The computationally-implemented method of claim 1, whereinthe determining an acceptability of an effect of content of the data atleast in part via at least two virtual machine representations of atleast a part of a real machine having at least one end-user specifiedpreference, at least one of the at least two virtual machinerepresentations operating at least in part on an individual core of amulti-core system comprises: determining an acceptability of an effectof content of the data at least in part via at least two virtual machinerepresentations of at least a portion of content of a real machine. 11.The computationally-implemented method of claim 1, wherein thedetermining an acceptability of an effect of content of the data atleast in part via at least two virtual machine representations of atleast a part of a real machine having at least one end-user specifiedpreference, at least one of the at least two virtual machinerepresentations operating at least in part on an individual core of amulti-core system comprises: determining an acceptability of an effectof content of the data at least in part via at least two virtual machinerepresentations of at least a portion of software of a real machine. 12.The computationally-implemented method of claim 1, wherein thedetermining an acceptability of an effect of content of the data atleast in part via at least two viral machine representations of at leasta part of a real machine having at least one end-user specifiedpreference, at least one of the at least two virtual machinerepresentations operating at least in part on an individual core of amulti-core system comprises: determining an acceptability of an effectof content of the data at least in part via at least two virtual machinerepresentations of at least a portion of hardware of a real machine. 13.The computationally-implemented method of claim 1, wherein thedetermining an acceptability of an effect of content of the data atleast in part via at least two virtual machine representations of atleast a part of a real machine having at least one end-user specifiedpreference, at least one of the at least two virtual machinerepresentations operating at least in part on an individual core of amulti-core system comprises: determining an acceptability of an effectof content of the data at least in part via at least two virtual machinerepresentations of at least a portion of an operating system of a realmachine.
 14. The computationally implemented method of claim 1, whereinthe determining an acceptability of an effect of content of the data atleast in part via at least two virtual machine representations of atleast a part of a real machine having at least one end-user specifiedpreference, at least one of the at least two virtual machinerepresentations operating at least in part on an individual core of amulti-core system comprises: determining an acceptability of an effectof content of the data at least in part via at least two virtual machinerepresentations of a real machine including at least a portion of acomputing device.
 15. The computationally implemented method of claim 1,wherein the determining an acceptability of an effect of content of thedata at least in part via at least two virtual machine representationsof at least a part of a real machine having at least one end-userspecified preference, at least one of the at least two virtual machinerepresentations operating at least in part on an individual core of amulti-core system comprises: determining an acceptability of an effectof content of the data at least in part via at least two virtual machinerepresentations of a real machine including at least one peripheraldevice.
 16. The computationally implemented method of claim 15, whereinthe determining an acceptability of an effect of content of the data atleast in part via at least two virtual machine representations of a realmachine including at least one peripheral device comprises: determiningan acceptability of an effect of content of the data at least in partvia at least two virtual machine representations of a real machineincluding at least one peripheral device that is at least one of aprinter, a fax machine, a peripheral memory device, a network adapter, amusic player, a cellular telephone, a data acquisition device, or adevice actuator.
 17. The computationally-implemented method of claim 1,wherein the determining an acceptability of an effect of content of thedata at least in part via at least two virtual machine representationsof at least a part of a real machine having at least one end-userspecified preference, at least one of the at least two virtual machinerepresentations operating at least in part on an individual core of amulti-core system comprises: determining a state of at least one of theat least two virtual machine representations operating at least in parton an individual core of a multi-core system prior to loading at least aportion of the data.
 18. The computationally-implemented method of claim1, wherein the determining an acceptability of an effect of content ofthe data at least in part via at least two virtual machinerepresentations of at least a part of a real machine having at least oneend-user specified preference, at least one of the at least two virtualmachine representations operating at least in part on an individual coreof a multi-core system comprises: determining a state of at least one ofthe at least two virtual machine representations operating at least inpart on an individual core of a multi-core system subsequent to loadingat least a portion of the data.
 19. The computationally-implementedmethod of claim 1, wherein the determining an acceptability of an effectof content of the data at least in part via at least two virtual machinerepresentations of at least a part of a real machine having at least oneend-user specified preference, at least one of the at least two virtualmachine representations operating at least in part on an individual coreof a multi-core system comprises: determining a state change of at leastone of the at least two virtual machine representations operating atleast in part on an individual core of a multi-core system between aprior state and a subsequent state of at least one of the at least twovirtual machine representations operating at least in part on anindividual core of a system comprising at least two cores after loadingat least a portion of the data.
 20. The computationally-implementedmethod of claim 19, wherein the determining a state change of at leastone of the at least two virtual machine representations operating atleast in part on an individual core of a multi-core system between aprior state and a subsequent state of at least one of the at least twovirtual machine representations operating at least in part on anindividual core of a system comprising at least two cores after loadingat least a portion of the data comprises: determining whether a statechange on at least one of the at least two viral machine representationsoperating at least in part on an individual core of a multi-core systemis an undesirable state change based on one or more end-user specifiedpreferences.
 21. The computationally-implemented method of claim 1,wherein the determining an acceptability of an effect of content of thedata at least in part via at least two virtual machine representationsof at least a part of a real machine having at least one end-userspecified preference, at least one of the at least two virtual machinerepresentations operating at least in part on an individual core of amulti-core system comprises: determining an acceptability of an effectof content of the data in response to at least one user setting.
 22. Thecomputationally-implemented method of claim 21, wherein the determiningan acceptability of an effect of content of the data in response to atleast one user setting comprises: determining an acceptability of aneffect of content of the data in response to a personal user setting.23. The computationally-implemented method of claim 21, wherein thedetermining an acceptability of an effect of content of the data inresponse to at least one user setting comprises: determining anacceptability of an effect of content of the data in response to a peeruser setting.
 24. The computationally-implemented method of claim 21,wherein the determining an acceptability of an effect of content of thedata in response to at least one user setting comprises: determining anacceptability of an effect of content of the data in response to acorporate user setting.
 25. The computationally-implemented method ofclaim 21, wherein the determining an acceptability of an effect ofcontent of the data in response to at least one user setting comprises:determining an acceptability of an effect of content of the data inresponse to a work safety user setting.
 26. Thecomputationally-implemented method of claim 21, wherein the determiningan acceptability of an effect of content of the data in response to atleast one user setting comprises: determining an acceptability of aneffect of content of the data in response to a desirability setting. 27.The computationally-implemented method of claim 26, wherein thedetermining an acceptability of an effect of content of the data inresponse to a desirability setting comprises: determining anacceptability of an effect of content of the data in response to areligious desirability setting.
 28. The computationally-implementedmethod of claim 26, wherein the determining an acceptability of aneffect of content of the data in response to a desirability settingcomprises: determining an acceptability of an effect of content of thedata in response to a political desirability setting.
 29. Thecomputationally-implemented method of claim 26, wherein the determiningan acceptability of an effect of content of the data in response to adesirability setting comprises: determining an acceptability of aneffect of content of the data in response to a cultural desirabilitysetting.
 30. The computationally-implemented method of claim 26, whereinthe determining an acceptability of an effect of content of the data inresponse to a desirability setting comprises: determining anacceptability of an effect of content of the data in response to a themerelated desirability setting.
 31. The computationally-implemented methodof claim 26, wherein the determining an acceptability of an effect ofcontent of the data in response to a desirability setting comprises:determining an acceptability of an effect of content of the data inresponse to an age appropriateness desirability setting.
 32. Thecomputationally-implemented method of claim 1, wherein the determiningan acceptability of an effect of content of the data at least in partvia at least two virtual machine representations of at least a part of areal machine having at least one end-user specified preference, at leastone of the at least two virtual machine representations operating atleast in part on an individual core of a multi-core system comprises:determining an acceptability of an effect of content of the data inresponse to at least one privacy related setting.
 33. Thecomputationally-implemented method of claim 32, wherein the determiningan acceptability of an effect of content of the data in response to atleast one privacy related setting comprises: determining anacceptability of an effect of content of the data in response to a userspecific privacy related setting.
 34. The computationally-implementedmethod of claim 32, wherein the determining an acceptability of aneffect of content of the data in response to at least one privacyrelated setting comprises: determining an acceptability of an effect ofcontent of the data in response to a group privacy related setting. 35.The computationally-implemented method of claim 32, wherein thedetermining an acceptability of an effect of content of the data inresponse to at least one privacy related setting comprises: determiningan acceptability of an effect of content of the data in response to acorporate privacy related setting.
 36. The computationally-implementedmethod of claim 32, wherein the determining an acceptability of aneffect of content of the data in response to at least one privacyrelated setting comprises: determining an acceptability of an effect ofcontent of the data in response to transmitted user information.
 37. Thecomputationally-implemented method of claim 32, wherein the determiningan acceptability of an effect of content of the data in response to atleast one privacy related setting comprises: determining anacceptability of an effect of content of the data in response tocaptured user information.
 38. The computationally-implemented method ofclaim 32, wherein the determining an acceptability of an effect ofcontent of the data in response to at least one privacy related settingcomprises: determining an acceptability of an effect of content of thedata in response to exposed user information.
 39. Thecomputationally-implemented method of claim 1, wherein the determiningan acceptability of an effect of content of the data at least in partvia at least two virtual machine representations of at least a part of areal machine having at least one end-user specified preference, at leastone of the at least two virtual machine representations operating atleast in part on an individual core of a multi-core system comprises:determining an acceptability of an effect of content of the data inresponse to visually examining at least a portion of a data image on atleast one of the at least two virtual machine representations operatingat least in part on an individual core of a multi-core system.
 40. Thecomputationally-implemented method of claim 1, wherein the determiningan acceptability of an effect of content of the data at least in partvia at least two virtual machine representations of at least a part of areal machine having at least one end-user specified preference, at leastone of the at least two virtual machine representations operating atleast in part on an individual core of a multi-core system comprises:determining an acceptability of an effect of content of the data atleast in part via at least three virtual machine representations of atleast a part of a real machine having at least one end-user specifiedpreference, at least one of the at least three virtual machinerepresentations operating at least in part on an individual core of amulti-core system comprising at least three cores.
 41. Thecomputationally-implemented method of claim 1, wherein the displaying atleast one data display option based on the determining an acceptabilityof a content of the data comprises: providing a data display option ofdisplaying at least a portion of the data.
 42. Thecomputationally-implemented method of claim 1, wherein the displaying atleast one data display option based on the determining an acceptabilityof a content of the data comprises: providing a data display option ofnot displaying at least a portion of the data.
 43. Thecomputationally-implemented method of claim 1, wherein the displaying atleast one data display option based on the determining an acceptabilityof a content of the data comprises: providing a data display option ofdisplaying a modified version of the data.
 44. Thecomputationally-implemented method of claim 43, wherein the providing adata display option of displaying a modified version of the datacomprises: providing a data display option of obfuscating anobjectionable data portion.
 45. The computationally-implemented methodof claim 43, wherein the providing a data display option of displaying amodified version of the data comprises: providing a data display optionof anonymizing an objectionable data portion.
 46. Thecomputationally-implemented method of claim 43, wherein the providing adata display option of displaying a modified version of the datacomprises: providing a data display option of at least one of removing,altering or replacing an objectionable data portion.
 47. Thecomputationally-implemented method of claim 43, wherein the displayingat least one data display option based on the determining anacceptability of a content of the data comprises: providing a datadisplay option of displaying a data portion consistent with at least oneuser-related setting.
 48. The computationally-implemented method ofclaim 47, wherein the providing a data display option of displaying adata portion consistent with at least one user-related settingcomprises: providing a data display option of displaying a data portionconsistent with a privacy related user setting.
 49. Thecomputationally-implemented method of claim 47, wherein the providing adata display option of displaying a data portion consistent with atleast one user-related setting comprises: providing a data displayoption of displaying a data portion consistent with a desirabilitysetting.
 50. The computationally-implemented method of claim 47, whereinthe providing a data display option of displaying a data portionconsistent with at least one user-related setting comprises: providing adata display option of displaying a data portion consistent with aworkplace established setting.
 51. The computationally-implementedmethod of claim 47, wherein the providing a data display option ofdisplaying a data portion consistent with at least one user-relatedsetting comprises: providing a data display option of displaying a dataportion consistent with a safety setting.
 52. Thecomputationally-implemented method of claim 51, wherein the providing adata display option of displaying a data portion consistent with asafety setting comprises: providing a data display option of displayinga data portion consistent with a public safety setting.
 53. Thecomputationally-implemented method of claim 51, wherein the providing adata display option of displaying a data portion consistent with asafety setting comprises: providing a data display option of displayinga data portion consistent with a home safety setting.
 54. Thecomputationally-implemented method of claim 51, wherein the providing adata display option of displaying a data portion consistent with asafety setting comprises: providing a data display option of displayinga data portion consistent with a workplace safety setting.
 55. Thecomputationally-implemented method of claim 51, wherein the providing adata display option of displaying a data portion consistent with asafety setting comprises: providing a data display option of displayinga data portion consistent with a child safety setting.
 56. Thecomputationally-implemented method of claim 1, wherein the displaying atleast one data display option based on the determining an acceptabilityof a content of the data comprises: redirecting to alternative data. 57.The computationally-implemented method of claim 56, wherein theredirecting to alternative data comprises: automatically redirecting toalternative data.
 58. The computationally-implemented method of claim56, wherein the redirecting to alternative data comprises: providing alist of selectable alternative data options.
 59. Thecomputationally-implemented method of claim 56, wherein the redirectingto alternative data comprises: displaying alternative data consistentwith a privacy related setting.
 60. The computationally-implementedmethod of claim 56, wherein the redirecting to alternative datacomprises: displaying alternative data consistent with a customized usersetting.
 61. The computationally-implemented method of claim 56, whereinthe redirecting to alternative data comprises: displaying alternativedata consistent with a desirability setting.
 62. Thecomputationally-implemented method of claim 56, wherein the redirectingto alternative data comprises: displaying alternative data consistentwith a workplace established setting.
 63. Thecomputationally-implemented method of claim 56, wherein the redirectingto alternative data comprises: displaying alternative data consistentwith a user history setting.
 64. The computationally-implemented methodof claim 56, wherein the redirecting to alternative data comprises:displaying alternative data consistent with a safety setting.
 65. Thecomputationally-implemented method of claim 64, wherein the displayingalternative data consistent with a safety setting comprises: displayingalternative data consistent with a public safety setting.
 66. Thecomputationally-implemented method of claim 64, wherein the displayingalternative data consistent with a safety setting comprises: displayingalternative data consistent with a home safety setting.
 67. Thecomputationally-implemented method of claim 64, wherein the displayingalternative data consistent with a safety setting comprises: displayingalternative data consistent with a workplace safety setting.
 68. Thecomputationally-implemented method of claim 64, wherein the displayingalternative data consistent with a safety setting comprises: displayingalternative data consistent with a child safety setting.
 69. Acomputationally-implemented system comprising: means for obtaining atleast a portion of data from a data source; means for determining acontent of the data; means for determining an acceptability of an effectof content of the data at least in part via at least two virtual machinerepresentations of at least a part of a real machine having at least oneend-user specified preference, at least one of the at least two virtualmachine representations operating at least in part on an individual coreof a multi-core system; and means for displaying at least one datadisplay option based on the determining an acceptability of a content ofthe data.
 70. The computationally-implemented system of claim 69,wherein the means for determining a content of the data comprises: meansfor examining a database of known content for data content information.71. The computationally-implemented system of claim 69, wherein themeans for determining a content of the data comprises: means fortraversing at least a portion of the data in real time.
 72. Thecomputationally-implemented system of claim 69, wherein the means fordetermining a content of the data comprises: means for locally examiningat least a portion of the data.
 73. The computationally-implementedsystem of claim 69, wherein the means for determining an acceptabilityof an effect of content of the data at least in part via at least twovirtual machine representations of at least a part of a real machinehaving at least one end-user specified preference, at least one of theat least two virtual machine representations operating at least in parton an individual core of a multi-core system comprises: means forexamining at least a portion of the data to locate references toadditional content.
 74. The computationally-implemented system of claim73, wherein the means for examining at least a portion of the data tolocate references to additional content comprises: means for determiningwhether the data references additional data content information whenloading.
 75. The computationally-implemented system of claim 73, whereinthe means for examining at least a portion of the data to locatereferences to additional content comprises: means for issuing a requestto a remote computer for additional data content information.
 76. Thecomputationally-implemented system of claim 69, wherein the means fordetermining an acceptability of an effect of content of the data atleast in part via at least two virtual machine representations of atleast a part of a real machine having at least one end-user specifiedpreference, at least one of the at least two virtual machinerepresentations operating at least in part on an individual core of amulti-core system comprises: means for determining an acceptability ofan effect of content of the data at least in part via at least twovirtual machine representations of at least a part of a real machinehaving at least one end-user specified preference, at least one of theat least two virtual machine representations operating at least in parton an individual core of a multi-core system at least partially residentwithin a real machine.
 77. The computationally-implemented system ofclaim 69, wherein the means for determining an acceptability of aneffect of content of the data at least in part via at least two virtualmachine representations of at least a part of a real machine having atleast one end-user specified preference, at least one of the at leasttwo virtual machine representations operating at least in part on anindividual core of a multi-core system comprises: means for determiningan acceptability of an effect of content of the data at least in partvia at least two virtual machine representations of at least a part of areal machine having at least one end-user specified preference, at leastone of the at least two virtual machine representations operating atleast in part on an individual core of a multi-core system at leastpartially non-resident within a real machine.
 78. Thecomputationally-implemented system of claim 69, wherein the means fordetermining an acceptability of an effect of content of the data atleast in part via at least two virtual machine representations of atleast a part of a real machine having at least one end-user specifiedpreference, at least one of the at least two virtual machinerepresentations operating at least in part on an individual core of amulti-core system comprises: means for determining an acceptability ofan effect of content of the data at least in part via at least twovirtual machine representations of at least a portion of content of areal machine.
 79. The computationally-implemented system of claim 69,wherein the means for determining an acceptability of an effect ofcontent of the data at least in part via at least two virtual machinerepresentations of at least a part of a real machine having at least oneend-user specified preference, at least one of the at least two virtualmachine representations operating at least in part on an individual coreof a multi-core system comprises: means for determining an acceptabilityof an effect of content of the data at least in part via at least twovirtual machine representations of at least a portion of software of areal machine.
 80. The computationally-implemented system of claim 69,wherein the means for determining an acceptability of an effect ofcontent of the data at least in part via at least two virtual machinerepresentations of at least a part of a real machine having at least oneend-user specified preference, at least one of the at least two virtualmachine representations operating at least in part on an individual coreof a multi-core system comprises: means for determining an acceptabilityof an effect of content of the data at least in part via at least twovirtual machine representations of at least a portion of hardware of areal machine.
 81. The computationally-implemented system of claim 69,wherein the means for determining an acceptability of an effect ofcontent of the data at least in part via at least two virtual machinerepresentations of at least a part of a real machine having at least oneend-user specified preference, at least one of the at least two virtualmachine representations operating at least in part on an individual coreof a multi-core system comprises: means for determining an acceptabilityof an effect of content of the data at least in part via at least twovirtual machine representations of at least a portion of an operatingsystem of a real machine.
 82. The computationally implemented system ofclaim 69, wherein the means for determining an acceptability of aneffect of content of the data at least in part via at least two virtualmachine representations of at least a part of a real machine having atleast one end-user specified preference, at least one of the at leasttwo virtual machine representations operating at least in part on anindividual core of a multi-core system comprises: means for determiningan acceptability of an effect of content of the data at least in partvia at least two virtual machine representations of a real machineincluding at least a portion of a computing device.
 83. Thecomputationally implemented system of claim 69, wherein the means fordetermining an acceptability of an effect of content of the data atleast in part via at least two virtual machine representations of atleast a part of a real machine having at least one end-user specifiedpreference, at least one of the at least two virtual machinerepresentations operating at least in part on an individual core of amulti-core system comprises: means for determining an acceptability ofan effect of content of the data at least in part via at least twovirtual machine representations of a real machine including at least oneperipheral device.
 84. The computationally implemented system of claim83, wherein the means for determining an acceptability of an effect ofcontent of the data at least in part via at least two virtual machinerepresentations of a real machine including at least one peripheraldevice comprises: means for determining an acceptability of an effect ofcontent of the data at least in part via at least two virtual machinerepresentations of a real machine including at least one peripheraldevice that is at least one of a printer, a fax machine, a peripheralmemory device, a network adapter, a music player, a cellular telephone,a data acquisition device, or a device actuator.
 85. Thecomputationally-implemented system of claim 69, wherein the means fordetermining an acceptability of an effect of content of the data atleast in part via at least two vial machine representations of at leasta part of a real machine having at least one end-user specifiedpreference, at least one of the at least two virtual machinerepresentations operating at least in part on an individual core of amulti-core system comprises: means for determining a state of at leastone of the at least two virtual machine representations operating atleast in part on an individual core of a multi-core system prior toloading at least a portion of the data.
 86. Thecomputationally-implemented system of claim 69, wherein the means fordetermining an acceptability of an effect of content of the data atleast in part via at least two virtual machine representations of atleast a part of a real machine having at least one end-user specifiedpreference, at least one of the at least two virtual machinerepresentations operating at least in part on an individual core of amulti-core system comprises: means for determining a state of at leastone of the at least two virtual machine representations operating atleast in part on an individual core of a multi-core system subsequent toloading at least a portion of the data.
 87. Thecomputationally-implemented system of claim 69, wherein the means fordetermining an acceptability of an effect of content of the data atleast in part via at least two virtual machine representations of atleast a part of a real machine having at least one end-user specifiedpreference, at least one of the at least two virtual machinerepresentations operating at least in part on an individual core of amulti-core system comprises: means for determining a state change of atleast one of the at least two virtual machine representations operatingat least in part on an individual core of a multi-core system between aprior state and a subsequent state of at least one of the at least twovirtual machine representations operating at least in part on anindividual core of a system comprising at least two cores after loadingat least a portion of the data.
 88. The computationally-implementedsystem of claim 87, wherein the means for determining a state change ofat least one of the at least two virtual machine representationsoperating at least in part on an individual core of a multi-core systembetween a prior state and a subsequent state of at least one of the atleast two virtual machine representations operating at least in part onan individual core of a system comprising at least two cores afterloading at least a portion of the data comprises: means for determiningwhether a state change on at least one of the at least two virtualmachine representations operating at least in part on an individual coreof a multi-core system is an undesirable state change based on one ormore end-user specified preferences.
 89. The computationally-implementedsystem of claim 69, wherein the means for determining an acceptabilityof an effect of content of the data at least in part via at least twovirtual machine representations of at least a part of a real machinehaving at least one end-user specified preference, at least one of theat least two virtual machine representations operating at least in parton an individual core of a multi-core system comprises: means fordetermining an acceptability of an effect of content of the data inresponse to at least one user setting.
 90. Thecomputationally-implemented system of claim 89, wherein the means fordetermining an acceptability of an effect of content of the data inresponse to at least one user setting comprises: means for determiningan acceptability of an effect of content of the data in response to apersonal user setting.
 91. The computationally-implemented system ofclaim 89, wherein the means for determining an acceptability of aneffect of content of the data in response to at least one user settingcomprises: means for determining an acceptability of an effect ofcontent of the data in response to a peer user setting.
 92. Thecomputationally-implemented system of claim 89, wherein the means fordetermining an acceptability of an effect of content of the data inresponse to at least one user setting comprises: means for determiningan acceptability of an effect of content of the data in response to acorporate user setting.
 93. The computationally-implemented system ofclaim 89, wherein the means for determining an acceptability of aneffect of content of the data in response to at least one user settingcomprises: means for determining an acceptability of an effect ofcontent of the data in response to a work safety user setting.
 94. Thecomputationally-implemented system of claim 89, wherein the means fordetermining an acceptability of an effect of content of the data inresponse to at least one user setting comprises: means for determiningan acceptability of an effect of content of the data in response to adesirability setting.
 95. The computationally-implemented system ofclaim 94, wherein the means for determining an acceptability of aneffect of content of the data in response to a desirability settingcomprises: means for determining an acceptability of an effect ofcontent of the data in response to a religious desirability setting. 96.The computationally-implemented system of claim 94, wherein the meansfor determining an acceptability of an effect of content of the data inresponse to a desirability setting comprises: means for determining anacceptability of an effect of content of the data in response to apolitical desirability setting.
 97. The computationally-implementedsystem of claim 94, wherein the means for determining an acceptabilityof an effect of content of the data in response to a desirabilitysetting comprises: means for determining an acceptability of an effectof content of the data in response to a cultural desirability setting.98. The computationally-implemented system of claim 94, wherein themeans for determining an acceptability of an effect of content of thedata in response to a desirability setting comprises: means fordetermining an acceptability of an effect of content of the data inresponse to a theme related desirability setting.
 99. Thecomputationally-implemented system of claim 94, wherein the means fordetermining an acceptability of an effect of content of the data inresponse to a desirability setting comprises: means for determining anacceptability of an effect of content of the data in response to an ageappropriateness desirability setting.
 100. Thecomputationally-implemented system of claim 69, wherein the means fordetermining an acceptability of an effect of content of the data atleast in part via at least two virtual machine representations of atleast a part of a real machine having at least one end-user specifiedpreference, at least one of the at least two virtual machinerepresentations operating at least in part on an individual core of amulti-core system comprises: means for determining an acceptability ofan effect of content of the data in response to at least one privacyrelated setting.
 101. The computationally-implemented system of claim100, wherein the means for determining an acceptability of an effect ofcontent of the data in response to at least one privacy related settingcomprises: means for determining an acceptability of an effect ofcontent of the data in response to a user specific privacy relatedsetting.
 102. The computationally-implemented system of claim 100,wherein the means for determining an acceptability of an effect ofcontent of the data in response to at least one privacy related settingcomprises: means for determining an acceptability of an effect ofcontent of the data in response to a group privacy related setting. 103.The computationally-implemented system of claim 100, wherein the meansfor determining an acceptability of an effect of content of the data inresponse to at least one privacy related setting comprises: means fordetermining an acceptability of an effect of content of the data inresponse to a corporate privacy related setting.
 104. Thecomputationally-implemented system of claim 100, wherein the means fordetermining an acceptability of an effect of content of the data inresponse to at least one privacy related setting comprises: means fordetermining an acceptability of an effect of content of the data inresponse to transmitted user information.
 105. Thecomputationally-implemented system of claim 100, wherein the means fordetermining an acceptability of an effect of content of the data inresponse to at least one privacy related setting comprises: means fordetermining an acceptability of an effect of content of the data inresponse to captured user information.
 106. Thecomputationally-implemented system of claim 100, wherein the means fordetermining an acceptability of an effect of content of the data inresponse to at least one privacy related setting comprises: means fordetermining an acceptability of an effect of content of the data inresponse to exposed user information.
 107. Thecomputationally-implemented system of claim 69, wherein the means fordetermining an acceptability of an effect of content of the data atleast in part via at least two virtual machine representations of atleast a part of a real machine having at least one end-user specifiedpreference, at least one of the at least two virtual machinerepresentations operating at least in part on an individual core of amulti-core system comprises: means for determining an acceptability ofan effect of content of the data in response to visually examining atleast a portion of a data image on at least one of the at least twovirtual machine representations operating at least in part on anindividual core of a multi-core system.
 108. Thecomputationally-implemented system of claim 69, wherein the means fordetermining an acceptability of an effect of content of the data atleast in part via at least two virtual machine representations of atleast a part of a real machine having at least one end-user specifiedpreference, at least one of the at least two virtual machinerepresentations operating at least in part on an individual core of amulti-core system comprises: means for determining an acceptability ofan effect of content of the data at least in part via at least threevirtual machine representations of at least a part of a real machinehaving at least one end-user specified preference, at least one of theat least three virtual machine representations operating at least inpart on an individual core of a multi-core system comprising at leastthree cores.
 109. The computationally-implemented system of claim 69,wherein the means for displaying at least one data display option basedon the determining an acceptability of a content of the data comprises:means for providing a data display option of displaying at least aportion of the data.
 110. The computationally-implemented system ofclaim 69, wherein the means for displaying at least one data displayoption based on the determining an acceptability of a content of thedata comprises: means for providing a data display option of notdisplaying at least a portion of the data.
 111. Thecomputationally-implemented system of claim 69, wherein the means fordisplaying at least one data display option based on the determining anacceptability of a content of the data comprises: means for providing adata display option of displaying a modified version of the data. 112.The computationally-implemented system of claim 111, wherein the meansfor providing a data display option of displaying a modified version ofthe data comprises: means for providing a data display option ofobfuscating an objectionable data portion.
 113. Thecomputationally-implemented system of claim 111, wherein the means forproviding a data display option of displaying a modified version of thedata comprises: means for providing a data display option of anonymizingan objectionable data portion.
 114. The computationally-implementedsystem of claim 111, wherein the means for providing a datadisplay-option of displaying a modified version of the data comprises:means for providing a data display option of at least one of removing,altering or replacing an objectionable data portion.
 115. Thecomputationally-implemented system of claim 111, wherein the means fordisplaying at least one data display option based on the determining anacceptability of a content of the data comprises: means for providing adata display option of displaying a data portion consistent with atleast one user-related setting.
 116. The computationally-implementedsystem of claim 115, wherein the means for providing a data displayoption of displaying a data portion consistent with at least oneuser-related setting comprises: means for providing a data displayoption of displaying a data portion consistent with a privacy relateduser setting.
 117. The computationally-implemented system of claim 115,wherein the means for providing a data display option of displaying adata portion consistent with at least one user-related settingcomprises: means for providing a data display option of displaying adata portion consistent with a desirability setting.
 118. Thecomputationally-implemented system of claim 115, wherein the means forproviding a data display option of displaying a data portion consistentwith at least one user-related setting comprises: means for providing adata display option of displaying a data portion consistent with aworkplace established setting.
 119. The computationally-implementedsystem of claim 115, wherein the means for providing a data displayoption of displaying a data portion consistent with at least oneuser-related setting comprises: means for providing a data displayoption of displaying a data portion consistent with a safety setting.120. The computationally-implemented system of claim 119, wherein themeans for providing a data display option of displaying a data portionconsistent with a safety setting comprises: means for providing a datadisplay option of displaying a data portion consistent with a publicsafety setting.
 121. The computationally-implemented system of claim119, wherein the means for providing a data display option of displayinga data portion consistent with a safety setting comprises: means forproviding a data display option of displaying a data portion consistentwith a home safety setting.
 122. The computationally-implemented systemof claim 119, wherein the means for providing a data display option ofdisplaying a data portion consistent with a safety setting comprises:means for providing a data display option of displaying a data portionconsistent with a workplace safety setting.
 123. Thecomputationally-implemented system of claim 119, wherein the means forproviding a data display option of displaying a data portion consistentwith a safety setting comprises: means for providing a data displayoption of displaying a data portion consistent with a child safetysetting.
 124. The computationally-implemented system of claim 69,wherein the means for displaying at least one data display option basedon the determining an acceptability of a content of the data comprises:means for redirecting to alternative data.
 125. Thecomputationally-implemented system of claim 124, wherein the means forredirecting to alternative data comprises: means for automaticallyredirecting to alternative data.
 126. The computationally-implementedsystem of claim 124, wherein the means for redirecting to alternativedata comprises: means for providing a list of selectable alternativedata options.
 127. The computationally-implemented system of claim 124,wherein the means for redirecting to alternative data comprises: meansfor displaying alternative data consistent with a privacy relatedsetting.
 128. The computationally-implemented system of claim 124,wherein the means for redirecting to alternative data comprises: meansfor displaying alternative data consistent with a customized usersetting.
 129. The computationally-implemented system of claim 124,wherein the means for redirecting to alternative data comprises: meansfor displaying alternative data consistent with a desirability setting.130. The computationally-implemented system of claim 124, wherein themeans for redirecting to alternative data comprises: means fordisplaying alternative data consistent with a workplace establishedsetting.
 131. The computationally-implemented system of claim 124,wherein the means for redirecting to alternative data comprises: meansfor displaying alternative data consistent with a user history setting.132. The computationally-implemented system of claim 124, wherein themeans for redirecting to alternative data comprises: means fordisplaying alternative data consistent with a safety setting.
 133. Thecomputationally-implemented system of claim 132, wherein the means fordisplaying alternative data consistent with a safety setting comprises:means for displaying alternative data consistent with a public safetysetting.
 134. The computationally-implemented system of claim 132,wherein the means for displaying alternative data consistent with asafety setting comprises: means for displaying alternative dataconsistent with a home safety setting.
 135. Thecomputationally-implemented system of claim 132, wherein the means fordisplaying alternative data consistent with a safety setting comprises:means for displaying alternative data consistent with a workplace safetysetting.
 136. The computationally-implemented system of claim 132,wherein the means for displaying alternative data consistent with asafety setting comprises: means for displaying alternative dataconsistent with a child safety setting.
 137. Acomputationally-implemented system comprising: circuitry for obtainingat least a portion of data from a data source; circuitry for determininga content of the data; circuitry for determining an acceptability of aneffect of content of the data at least in part via at least two virtualmachine representations of at least a part of a real machine having atleast one end-user specified preference, at least one of the at leasttwo virtual machine representations operating at least in part on anindividual core of a multi-core system; and circuitry for displaying atleast one data display option based on the determining an acceptabilityof a content of the data.